Color Visualization

Class: ColorVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.color.v1.ColorVisualizationBlockV1

Fill detected objects with solid colors using customizable color palettes, creating color-coded overlays that distinguish different objects or classes while preserving image details through opacity blending.

How This Block Works

This block takes an image and detection predictions and fills the detected object regions with solid colors. The block:

1. Takes an image and predictions as input
2. Identifies detected regions from bounding boxes or segmentation masks
3. Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
4. Fills detected object regions with solid colors using Supervision's ColorAnnotator
5. Blends the colored overlay with the original image based on the opacity setting
6. Returns an annotated image where detected objects are filled with colors, while the rest of the image remains unchanged

The block works with both object detection predictions (using bounding boxes) and instance segmentation predictions (using masks). When masks are available, it fills the exact shape of detected objects; otherwise, it fills rectangular bounding box regions. Colors are assigned from the selected palette based on the color axis setting (class, index, or track ID), allowing different objects or classes to be distinguished by color. The opacity parameter controls how transparent the color overlay is, allowing you to create effects ranging from subtle color tinting (low opacity) where original image details remain visible, to solid color fills (high opacity) that completely replace object appearance.

Common Use Cases

• Color-Coded Object Classification: Fill detected objects with different colors based on their class, category, or classification results to create intuitive color-coded visualizations for quick object identification and categorization
• Multi-Object Tracking Visualization: Color-code tracked objects with distinct colors based on their tracking IDs to visualize object trajectories, track persistence, or distinguish multiple tracked objects across frames
• Visual Category Distinction: Use different colors for different object categories or types (e.g., vehicles, people, products) to create clear visual distinctions in monitoring, surveillance, or inventory management workflows
• Mask-Based Segmentation Display: Fill segmented regions with colors to visualize instance segmentation results, highlight segmented objects, or create colored mask overlays for analysis or presentation
• Interactive Visualization and UI: Create color-coded visualizations for user interfaces, dashboards, or interactive applications where color-coding provides intuitive visual feedback or object grouping
• Presentation and Reporting: Generate color-filled visualizations for reports, documentation, or presentations where color-coding helps distinguish object types, highlight specific categories, or create visually appealing detection displays

Connecting to Other Blocks

The annotated image from this block can be connected to:

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

Type identifier

Use the following identifier in step "type" field: roboflow_core/color_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..	✅
opacity	float	Opacity of the color overlay, ranging from 0.0 (fully transparent, original object appearance visible) to 1.0 (fully opaque, solid color fill). Values between 0.0 and 1.0 create a blend between the original image and the color overlay. Lower values create subtle color tinting where object details remain visible, while higher values create stronger color fills that obscure original object 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 Color Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Color 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, rle_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..
  • opacity (float_zero_to_one): Opacity of the color overlay, ranging from 0.0 (fully transparent, original object appearance visible) to 1.0 (fully opaque, solid color fill). Values between 0.0 and 1.0 create a blend between the original image and the color overlay. Lower values create subtle color tinting where object details remain visible, while higher values create stronger color fills that obscure original object appearance..

• output

  • image (image): Image in workflows.

Example JSON definition of step Color Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/color_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",
    "opacity": 0.5
}