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

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, object_detection_prediction, keypoint_detection_prediction, instance_segmentation_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' (object area in pixels), 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
}