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:
Crop Visualization,Image Slicer,Detection Event Log,Roboflow Dataset Upload,ByteTrack Tracker,Instance Segmentation Model,Google Gemini,Classification Label Visualization,Mask Edge Snap,Dynamic Crop,Per-Class Confidence Filter,Detections Transformation,Time in Zone,SAM 3 Interactive,Velocity,Image Slicer,Absolute Static Crop,Detections Classes Replacement,Template Matching,Current Time,Pixel Color Count,Roboflow Custom Metadata,Multi-Label Classification Model,SIFT Comparison,Mask Area Measurement,Morphological Transformation,Background Subtraction,Line Counter Visualization,BoT-SORT Tracker,Ellipse Visualization,OpenAI,MoonshotAI Kimi,SAM 3,Grid Visualization,PLC ModbusTCP,Bounding Box Visualization,Morphological Transformation,Segment Anything 2 Model,Google Gemma,Byte Tracker,Object Detection Model,Motion Detection,Cosmos 3,Overlap Filter,OpenAI,Webhook Sink,VLM As Detector,Path Deviation,Size Measurement,Depth Estimation,Image Preprocessing,OPC UA Writer Sink,Detections Stabilizer,Bounding Rectangle,SAM 3,Line Counter,Stitch Images,Roboflow Dataset Upload,OpenAI,SAM2 Video Tracker,Camera Calibration,Google Vision OCR,OpenAI,QR Code Generator,Florence-2 Model,Polygon Zone Visualization,Contrast Equalization,Detection Offset,SAM3 Video Tracker,VLM As Classifier,Detections List Roll-Up,Clip Comparison,Pixelate Visualization,SORT Tracker,Qwen 3.6 API,PLC Writer,CSV Formatter,Detections Stitch,Track Class Lock,Triangle Visualization,Stability AI Image Generation,Icon Visualization,Dot Visualization,Stability AI Outpainting,Keypoint Visualization,Byte Tracker,Relative Static Crop,Anthropic Claude,PP-OCR,Anthropic Claude,Image Stack,Roboflow Visual Search Classifier,VLM As Detector,OCR Model,SAM 3,GeoTag Detection,PTZ Tracking (ONVIF),Qwen 3.5 API,Halo Visualization,OpenRouter,Frame Delay,Local File Sink,Text Display,Roboflow Asset Library Attributes,Image Threshold,Image Blur,Gaze Detection,Color Visualization,LMM,Corner Visualization,JSON Parser,S3 Sink,MQTT Writer,Qwen-VL,Instance Segmentation Model,Google Gemini,Slack Notification,Stitch OCR Detections,Event Writer,Detections Consensus,Identify Outliers,SIFT,Nearest Neighbor Detection Match,VLM As Classifier,Auto Rotate on Edges,Time in Zone,Stitch OCR Detections,Google Gemini,Instance Segmentation Model,Dimension Collapse,Twilio SMS Notification,Trace Visualization,Single-Label Classification Model,Perspective Correction,Seg Preview,Camera Focus,EasyOCR,Google Gemma API,PLC EthernetIP,Object Detection Model,Instance Segmentation Model,OC-SORT Tracker,MoonshotAI Kimi,Distance Measurement,Twilio SMS/MMS Notification,Blur Visualization,Path Deviation,LMM For Classification,Polygon Visualization,Model Monitoring Inference Aggregator,Stability AI Inpainting,Image Convert Grayscale,Microsoft SQL Server Sink,Florence-2 Model,Qwen3.5-VL,Moondream2,Clip Comparison,Dynamic Zone,Mask Visualization,Reference Path Visualization,Email Notification,Polygon Visualization,Roboflow Visual Search,SIFT Comparison,Halo Visualization,Contrast Enhancement,Keypoint Detection Model,Llama 3.2 Vision,Detections Combine,GLM-OCR,CogVLM,Circle Visualization,Byte Tracker,Image Contours,Camera Focus,Heatmap Visualization,Roboflow Vision Events,Line Counter,YOLO-World Model,Llama 3.2 Vision,OpenAI-Compatible LLM,Label Visualization,Time in Zone,Background Color Visualization,Keypoint Detection Model,PLC Reader,Buffer,Model Comparison Visualization,Keypoint Detection Model,Detections Merge,Detections Filter,Object Detection Model,Email Notification,Anthropic Claude,Identify Changes - outputs:
Crop Visualization,Image Slicer,Roboflow Dataset Upload,ByteTrack Tracker,Instance Segmentation Model,Google Gemini,Classification Label Visualization,Mask Edge Snap,Dynamic Crop,SAM 3 Interactive,Image Slicer,Absolute Static Crop,Template Matching,Pixel Color Count,Multi-Label Classification Model,SIFT Comparison,Morphological Transformation,Background Subtraction,Line Counter Visualization,BoT-SORT Tracker,Ellipse Visualization,MoonshotAI Kimi,Multi-Label Classification Model,OpenAI,SAM 3,Grid Visualization,Segment Anything 2 Model,Bounding Box Visualization,Morphological Transformation,Google Gemma,Semantic Segmentation Model,Perception Encoder Embedding Model,Object Detection Model,Byte Tracker,Cosmos 3,Motion Detection,OpenAI,Multi-Label Classification Model,VLM As Detector,Depth Estimation,Image Preprocessing,Detections Stabilizer,Single-Label Classification Model,SAM 3,QR Code Detection,Stitch Images,Roboflow Dataset Upload,OpenAI,SAM2 Video Tracker,Qwen3.5,Camera Calibration,Google Vision OCR,OpenAI,Florence-2 Model,Qwen3-VL,Polygon Zone Visualization,Contrast Equalization,SAM3 Video Tracker,VLM As Classifier,Clip Comparison,Pixelate Visualization,SORT Tracker,Qwen 3.6 API,Detections Stitch,Track Class Lock,Triangle Visualization,Stability AI Image Generation,Icon Visualization,Dot Visualization,Stability AI Outpainting,Keypoint Visualization,Anthropic Claude,Relative Static Crop,PP-OCR,Anthropic Claude,Image Stack,Roboflow Visual Search Classifier,VLM As Detector,OCR Model,SAM 3,GeoTag Detection,Qwen 3.5 API,Halo Visualization,OpenRouter,Frame Delay,Semantic Segmentation Model,Text Display,Image Threshold,Image Blur,Gaze Detection,Color Visualization,LMM,Corner Visualization,Qwen-VL,Instance Segmentation Model,CLIP Embedding Model,Google Gemini,Event Writer,SIFT,VLM As Classifier,Google Gemini,Auto Rotate on Edges,Instance Segmentation Model,Single-Label Classification Model,Trace Visualization,Perspective Correction,Seg Preview,Camera Focus,EasyOCR,Google Gemma API,Object Detection Model,Instance Segmentation Model,OC-SORT Tracker,MoonshotAI Kimi,Twilio SMS/MMS Notification,Blur Visualization,Polygon Visualization,Stability AI Inpainting,Image Convert Grayscale,Florence-2 Model,Qwen3.5-VL,Barcode Detection,Moondream2,Dominant Color,Clip Comparison,Mask Visualization,Reference Path Visualization,Email Notification,Polygon Visualization,Roboflow Visual Search,Halo Visualization,SmolVLM2,Contrast Enhancement,Keypoint Detection Model,Llama 3.2 Vision,GLM-OCR,CogVLM,Circle Visualization,Image Contours,Camera Focus,Heatmap Visualization,Roboflow Vision Events,Llama 3.2 Vision,YOLO-World Model,Qwen2.5-VL,Label Visualization,Time in Zone,Background Color Visualization,Keypoint Detection Model,Buffer,Single-Label Classification Model,Model Comparison Visualization,Keypoint Detection Model,LMM For Classification,Object Detection Model,Anthropic Claude
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,object_detection_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..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
}