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:
- Takes an image and predictions as input
- Identifies detected regions from bounding boxes or segmentation masks
- Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
- Fills detected object regions with solid colors using Supervision's ColorAnnotator
- Blends the colored overlay with the original image based on the opacity setting
- 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:
Roboflow Asset Library Attributes,MoonshotAI Kimi,Path Deviation,Image Blur,Overlap Filter,Reference Path Visualization,SIFT Comparison,PTZ Tracking (ONVIF),Event Writer,Slack Notification,SAM2 Video Tracker,Halo Visualization,VLM As Classifier,Image Stack,Google Gemma,Qwen 3.6 API,Bounding Rectangle,Object Detection Model,Dot Visualization,Clip Comparison,Label Visualization,Background Color Visualization,Llama 3.2 Vision,Email Notification,SAM 3 Interactive,Velocity,Pixelate Visualization,OpenAI-Compatible LLM,Google Gemini,JSON Parser,Track Class Lock,Anthropic Claude,OpenAI,Trace Visualization,Llama 3.2 Vision,Detection Event Log,ByteTrack Tracker,Clip Comparison,Camera Focus,OpenAI,GLM-OCR,PLC ModbusTCP,Buffer,MQTT Writer,SIFT Comparison,Webhook Sink,CSV Formatter,Image Contours,Local File Sink,Motion Detection,Google Gemini,MoonshotAI Kimi,Polygon Visualization,Dimension Collapse,SIFT,Classification Label Visualization,Multi-Label Classification Model,Instance Segmentation Model,Keypoint Detection Model,Keypoint Visualization,Template Matching,Instance Segmentation Model,Icon Visualization,Seg Preview,Dynamic Crop,Stability AI Inpainting,Bounding Box Visualization,Polygon Zone Visualization,Stability AI Outpainting,Detections Transformation,Crop Visualization,BoT-SORT Tracker,Image Convert Grayscale,Byte Tracker,Mask Visualization,Halo Visualization,Detections Stitch,Detection Offset,Distance Measurement,SORT Tracker,PLC EthernetIP,Text Display,Morphological Transformation,Anthropic Claude,VLM As Classifier,Line Counter,Roboflow Dataset Upload,VLM As Detector,Detections Consensus,Object Detection Model,Detections Filter,Ellipse Visualization,Detections Merge,Keypoint Detection Model,SAM3 Video Tracker,Time in Zone,SAM 3,Size Measurement,Circle Visualization,Twilio SMS Notification,Path Deviation,S3 Sink,Email Notification,Camera Focus,Identify Changes,Byte Tracker,SAM 3,Image Slicer,LMM For Classification,OCR Model,Mask Area Measurement,Heatmap Visualization,OpenAI,Google Gemma API,Stitch Images,Identify Outliers,Time in Zone,Morphological Transformation,EasyOCR,YOLO-World Model,Current Time,Blur Visualization,Stitch OCR Detections,Moondream2,Detections List Roll-Up,Florence-2 Model,Google Gemini,Corner Visualization,OpenRouter,Detections Stabilizer,Pixel Color Count,Model Comparison Visualization,SAM 3,Model Monitoring Inference Aggregator,Google Vision OCR,Image Threshold,Byte Tracker,Instance Segmentation Model,LMM,Single-Label Classification Model,Polygon Visualization,Segment Anything 2 Model,Time in Zone,Stability AI Image Generation,Line Counter Visualization,Mask Edge Snap,Line Counter,CogVLM,Relative Static Crop,Qwen3.5-VL,Per-Class Confidence Filter,Grid Visualization,Image Preprocessing,Gaze Detection,Stitch OCR Detections,Anthropic Claude,OPC UA Writer Sink,Color Visualization,Dynamic Zone,Detections Combine,Triangle Visualization,QR Code Generator,Contrast Enhancement,Roboflow Dataset Upload,Absolute Static Crop,Qwen 3.5 API,Background Subtraction,OC-SORT Tracker,OpenAI,Image Slicer,Qwen-VL,Florence-2 Model,Perspective Correction,Twilio SMS/MMS Notification,Roboflow Vision Events,Microsoft SQL Server Sink,Instance Segmentation Model,Depth Estimation,Roboflow Custom Metadata,Contrast Equalization,Camera Calibration,Detections Classes Replacement,VLM As Detector,Keypoint Detection Model,Object Detection Model - outputs:
MoonshotAI Kimi,Image Blur,SmolVLM2,Reference Path Visualization,Event Writer,SAM2 Video Tracker,VLM As Classifier,Halo Visualization,CLIP Embedding Model,Image Stack,Clip Comparison,Google Gemma,Qwen 3.6 API,Object Detection Model,Dot Visualization,Label Visualization,Background Color Visualization,Llama 3.2 Vision,SAM 3 Interactive,Pixelate Visualization,Qwen3-VL,Google Gemini,Track Class Lock,Anthropic Claude,OpenAI,Trace Visualization,Llama 3.2 Vision,ByteTrack Tracker,Clip Comparison,Camera Focus,GLM-OCR,OpenAI,Qwen3.5,Buffer,QR Code Detection,SIFT Comparison,Image Contours,Motion Detection,Google Gemini,MoonshotAI Kimi,Polygon Visualization,SIFT,Classification Label Visualization,Multi-Label Classification Model,Instance Segmentation Model,Keypoint Detection Model,Template Matching,Keypoint Visualization,Instance Segmentation Model,Icon Visualization,Seg Preview,Dynamic Crop,Stability AI Inpainting,Bounding Box Visualization,BoT-SORT Tracker,Multi-Label Classification Model,Polygon Zone Visualization,Crop Visualization,Stability AI Outpainting,Image Convert Grayscale,Mask Visualization,Halo Visualization,Detections Stitch,SORT Tracker,Barcode Detection,Text Display,Anthropic Claude,Morphological Transformation,VLM As Classifier,Roboflow Dataset Upload,VLM As Detector,Object Detection Model,Ellipse Visualization,Keypoint Detection Model,SAM3 Video Tracker,SAM 3,Circle Visualization,Semantic Segmentation Model,Email Notification,Camera Focus,Single-Label Classification Model,SAM 3,Image Slicer,LMM For Classification,Dominant Color,OCR Model,Heatmap Visualization,Google Gemma API,OpenAI,Stitch Images,Morphological Transformation,EasyOCR,Single-Label Classification Model,YOLO-World Model,Blur Visualization,Moondream2,Florence-2 Model,Google Gemini,Corner Visualization,OpenRouter,Detections Stabilizer,Pixel Color Count,Model Comparison Visualization,SAM 3,Google Vision OCR,Byte Tracker,Image Threshold,Instance Segmentation Model,LMM,Single-Label Classification Model,Polygon Visualization,Segment Anything 2 Model,Time in Zone,Mask Edge Snap,Line Counter Visualization,Stability AI Image Generation,CogVLM,Relative Static Crop,Qwen3.5-VL,Image Preprocessing,Gaze Detection,Anthropic Claude,Color Visualization,Triangle Visualization,Roboflow Dataset Upload,Contrast Enhancement,Absolute Static Crop,Qwen 3.5 API,Background Subtraction,Multi-Label Classification Model,OC-SORT Tracker,OpenAI,Image Slicer,Semantic Segmentation Model,Qwen-VL,Florence-2 Model,Perspective Correction,Roboflow Vision Events,Twilio SMS/MMS Notification,Perception Encoder Embedding Model,Instance Segmentation Model,Depth Estimation,Contrast Equalization,Camera Calibration,VLM As Detector,Qwen2.5-VL,Keypoint Detection Model,Object Detection Model
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,keypoint_detection_prediction,rle_instance_segmentation_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..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
}