Label Visualization

Class: LabelVisualizationBlockV1

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

Draw text labels on detected objects with customizable content, position, styling, and background colors to display information like class names, confidence scores, tracking IDs, or other detection metadata.

How This Block Works

This block takes an image and detection predictions and draws text labels on each detected object. The block:

1. Takes an image and predictions as input
2. Extracts label text for each detection based on the selected text option (class name, confidence, tracker ID, dimensions, area, time in zone, or index)
3. Determines label position based on the selected anchor point (center, corners, edges, or center of mass)
4. Applies background color styling based on the selected color palette, with colors assigned by class, index, or track ID
5. Renders text labels with customizable text color, scale, thickness, padding, and border radius using Supervision's LabelAnnotator
6. Returns an annotated image with text labels overlaid on the original image

The block supports various text content options including class names, confidence scores, combination of class and confidence, tracker IDs (for tracked objects), time in zone (for zone analysis), object dimensions (center coordinates and width/height), area, or detection index. Labels are rendered with colored backgrounds that match the object's assigned color from the palette, and text styling (color, size, thickness) can be customized for optimal visibility. The labels can be positioned at any anchor point relative to each detection, allowing flexible placement for different visualization needs.

Common Use Cases

• Information Display on Detections: Add informative text labels showing class names, confidence scores, or other metadata directly on detected objects for quick identification and validation
• Model Performance Visualization: Display confidence scores or class predictions on detected objects to visualize model certainty, identify low-confidence detections, and validate model performance
• Object Tracking Visualization: Show tracker IDs on tracked objects to visualize object tracking across frames, monitor persistent object identities, or debug tracking algorithms
• Zone Analysis and Monitoring: Display "Time In Zone" labels on objects to visualize how long objects have been in specific zones for occupancy monitoring, dwell time analysis, or compliance tracking
• Spatial Information Display: Show object dimensions (center coordinates, width, height) or area measurements directly on detections for spatial analysis, measurement workflows, or quality control
• Professional Presentation and Reporting: Create clean, informative visualizations with labeled detections for reports, dashboards, or presentations that combine visual results with textual information

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Bounding Box Visualization, Polygon Visualization, Dot Visualization) to combine text labels with geometric annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save annotated images with labels for documentation, reporting, or analysis
• Webhook blocks to send visualized results with labels to external systems, APIs, or web applications for display in dashboards or monitoring tools
• Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with labels as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with labels for live monitoring, tracking visualization, or post-processing analysis

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	Content to display in text labels. Options: 'Class' (class name), 'Confidence' (confidence score), 'Class and Confidence' (both), 'Tracker Id' (tracking ID for tracked objects), 'Time In Zone' (time spent in zone), 'Dimensions' (center coordinates and width x height), 'Area' (bounding box area in pixels), 'Area (mask)' (mask area in pixels from Mask Area Measurement block), 'Area (converted)' (mask area in converted units from Mask Area Measurement block), or 'Index' (detection index)..	✅
text_position	str	Anchor position for placing labels relative to each detection's bounding box. Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object)..	✅
text_color	str	Color of the label text. Can be a color name (e.g., 'WHITE', 'BLACK') or color code in HEX format (e.g., '#FFFFFF') or RGB format (e.g., 'rgb(255, 255, 255)')..	✅
text_scale	float	Scale factor for text size. Higher values create larger text. Default is 1.0..	✅
text_thickness	int	Thickness of text characters in pixels. Higher values create bolder, thicker text for better visibility..	✅
text_padding	int	Padding around the text in pixels. Controls the spacing between the text and the label background border..	✅
border_radius	int	Border radius of the label background in pixels. Set to 0 for square corners. Higher values create more rounded corners for a softer appearance..	✅

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: Anthropic Claude, Camera Focus, Text Display, CSV Formatter, Segment Anything 2 Model, Dynamic Crop, Relative Static Crop, SAM 3, Camera Focus, Mask Visualization, SIFT, VLM As Classifier, Path Deviation, Detections Combine, Background Color Visualization, PTZ Tracking (ONVIF).md), Polygon Visualization, Moondream2, Stitch OCR Detections, Size Measurement, Line Counter, SIFT Comparison, Byte Tracker, Detections List Roll-Up, OpenAI, Identify Outliers, Crop Visualization, Single-Label Classification Model, Keypoint Visualization, Icon Visualization, Llama 3.2 Vision, Gaze Detection, OpenAI, Image Contours, Stitch Images, VLM As Classifier, Anthropic Claude, Distance Measurement, OpenAI, Triangle Visualization, Detections Classes Replacement, Background Subtraction, Reference Path Visualization, EasyOCR, Instance Segmentation Model, Line Counter Visualization, Slack Notification, Time in Zone, Line Counter, Detection Event Log, Google Gemini, JSON Parser, Mask Area Measurement, Image Convert Grayscale, Stability AI Image Generation, Pixel Color Count, Seg Preview, Time in Zone, OpenAI, Detections Merge, Stability AI Outpainting, Object Detection Model, Webhook Sink, Image Threshold, Instance Segmentation Model, Anthropic Claude, Buffer, Perspective Correction, SAM 3, Detection Offset, Clip Comparison, Clip Comparison, Halo Visualization, Image Blur, Email Notification, Contrast Equalization, Object Detection Model, Blur Visualization, VLM As Detector, Detections Stabilizer, Roboflow Dataset Upload, Corner Visualization, Classification Label Visualization, Dimension Collapse, Trace Visualization, Keypoint Detection Model, Multi-Label Classification Model, Stitch OCR Detections, YOLO-World Model, Bounding Rectangle, Local File Sink, Florence-2 Model, Ellipse Visualization, Pixelate Visualization, Roboflow Custom Metadata, Identify Changes, LMM, SAM 3, Circle Visualization, Dot Visualization, Twilio SMS Notification, SIFT Comparison, Byte Tracker, Velocity, Absolute Static Crop, Morphological Transformation, Template Matching, Overlap Filter, Dynamic Zone, Google Gemini, Polygon Visualization, Google Vision OCR, Color Visualization, LMM For Classification, Email Notification, Cosine Similarity, VLM As Detector, Bounding Box Visualization, Model Comparison Visualization, Grid Visualization, OCR Model, Halo Visualization, Detections Consensus, Stability AI Inpainting, Image Slicer, Florence-2 Model, Heatmap Visualization, Polygon Zone Visualization, Image Slicer, Detections Filter, Label Visualization, Google Gemini, Depth Estimation, Image Preprocessing, Detections Stitch, Time in Zone, CogVLM, Byte Tracker, Camera Calibration, Path Deviation, Detections Transformation, QR Code Generator, Motion Detection, Keypoint Detection Model, Model Monitoring Inference Aggregator, Twilio SMS/MMS Notification, Roboflow Dataset Upload
• outputs: Anthropic Claude, Clip Comparison, Qwen3-VL, Halo Visualization, Image Blur, Email Notification, CLIP Embedding Model, Camera Focus, Text Display, Contrast Equalization, Object Detection Model, Segment Anything 2 Model, Blur Visualization, VLM As Detector, Detections Stabilizer, Roboflow Dataset Upload, Dynamic Crop, Corner Visualization, Classification Label Visualization, Relative Static Crop, Keypoint Detection Model, Trace Visualization, Multi-Label Classification Model, SAM 3, Qwen2.5-VL, Mask Visualization, Camera Focus, SIFT, VLM As Classifier, YOLO-World Model, Background Color Visualization, Polygon Visualization, Moondream2, Barcode Detection, Florence-2 Model, Ellipse Visualization, SIFT Comparison, Pixelate Visualization, Single-Label Classification Model, LMM, SAM 3, OpenAI, Dot Visualization, Circle Visualization, Byte Tracker, Absolute Static Crop, Template Matching, Morphological Transformation, Dominant Color, Crop Visualization, Single-Label Classification Model, Google Gemini, Keypoint Visualization, Polygon Visualization, Google Vision OCR, Multi-Label Classification Model, Icon Visualization, Llama 3.2 Vision, Gaze Detection, OpenAI, Image Contours, Stitch Images, VLM As Classifier, Color Visualization, LMM For Classification, VLM As Detector, Anthropic Claude, SmolVLM2, OpenAI, Triangle Visualization, Bounding Box Visualization, Background Subtraction, Reference Path Visualization, EasyOCR, Instance Segmentation Model, Line Counter Visualization, Model Comparison Visualization, OCR Model, Halo Visualization, Time in Zone, Stability AI Inpainting, Image Slicer, Google Gemini, Florence-2 Model, Image Convert Grayscale, Stability AI Image Generation, Heatmap Visualization, Pixel Color Count, Seg Preview, Polygon Zone Visualization, Image Slicer, Label Visualization, Google Gemini, Depth Estimation, Image Preprocessing, Detections Stitch, OpenAI, Object Detection Model, Stability AI Outpainting, Buffer, QR Code Detection, Image Threshold, Anthropic Claude, Instance Segmentation Model, CogVLM, Perception Encoder Embedding Model, Camera Calibration, Perspective Correction, SAM 3, Motion Detection, Keypoint Detection Model, Twilio SMS/MMS Notification, Roboflow Dataset Upload, Clip Comparison

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[object_detection_prediction, instance_segmentation_prediction, rle_instance_segmentation_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): Content to display in text labels. Options: 'Class' (class name), 'Confidence' (confidence score), 'Class and Confidence' (both), 'Tracker Id' (tracking ID for tracked objects), 'Time In Zone' (time spent in zone), 'Dimensions' (center coordinates and width x height), 'Area' (bounding box area in pixels), 'Area (mask)' (mask area in pixels from Mask Area Measurement block), 'Area (converted)' (mask area in converted units from Mask Area Measurement block), or 'Index' (detection index)..
  • text_position (string): Anchor position for placing labels relative to each detection's bounding box. Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object)..
  • text_color (string): Color of the label text. Can be a color name (e.g., 'WHITE', 'BLACK') or color code in HEX format (e.g., '#FFFFFF') or RGB format (e.g., 'rgb(255, 255, 255)')..
  • text_scale (float): Scale factor for text size. Higher values create larger text. Default is 1.0..
  • text_thickness (integer): Thickness of text characters in pixels. Higher values create bolder, thicker text for better visibility..
  • text_padding (integer): Padding around the text in pixels. Controls the spacing between the text and the label background border..
  • border_radius (integer): Border radius of the label background in pixels. Set to 0 for square corners. Higher values create more rounded corners for a softer appearance..

• 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
}