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

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[object_detection_prediction, instance_segmentation_prediction, keypoint_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
}