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:
Detections Classes Replacement,Contrast Equalization,PTZ Tracking (ONVIF),Instance Segmentation Model,ByteTrack Tracker,Google Vision OCR,Grid Visualization,Identify Changes,SIFT,Trace Visualization,Twilio SMS/MMS Notification,QR Code Generator,SAM 3,GLM-OCR,SORT Tracker,OpenAI,Identify Outliers,Label Visualization,Pixelate Visualization,Twilio SMS Notification,OpenAI,Email Notification,Corner Visualization,Background Subtraction,Mask Area Measurement,Time in Zone,Morphological Transformation,SAM 3,Path Deviation,Line Counter,OpenAI,Clip Comparison,Background Color Visualization,Camera Calibration,Stability AI Outpainting,Ellipse Visualization,Detections Stitch,Dynamic Zone,VLM As Classifier,Heatmap Visualization,Velocity,Image Convert Grayscale,Detections Consensus,Detections Filter,Size Measurement,Color Visualization,Camera Focus,Image Slicer,SAM 3,Detections Combine,Polygon Visualization,Motion Detection,Blur Visualization,LMM,Slack Notification,Bounding Rectangle,Stability AI Inpainting,Detection Event Log,Perspective Correction,Instance Segmentation Model,Gaze Detection,Florence-2 Model,Anthropic Claude,Mask Visualization,Moondream2,Image Contours,Clip Comparison,Byte Tracker,YOLO-World Model,JSON Parser,Keypoint Detection Model,Object Detection Model,VLM As Detector,Pixel Color Count,Path Deviation,Dynamic Crop,Relative Static Crop,Line Counter,Byte Tracker,Email Notification,S3 Sink,Stability AI Image Generation,Model Comparison Visualization,Absolute Static Crop,Keypoint Visualization,Roboflow Dataset Upload,Buffer,Model Monitoring Inference Aggregator,Reference Path Visualization,Halo Visualization,OCR Model,SIFT Comparison,VLM As Classifier,Time in Zone,Detections Stabilizer,VLM As Detector,Image Preprocessing,Crop Visualization,Classification Label Visualization,Local File Sink,Qwen3.5-VL,Stitch OCR Detections,Stitch Images,Time in Zone,Stitch OCR Detections,LMM For Classification,EasyOCR,CSV Formatter,Image Threshold,Anthropic Claude,Google Gemini,Halo Visualization,Roboflow Custom Metadata,CogVLM,OpenAI,Single-Label Classification Model,Triangle Visualization,Image Blur,Depth Estimation,Detections Transformation,Dimension Collapse,SIFT Comparison,Overlap Filter,Text Display,Anthropic Claude,Dot Visualization,Template Matching,Keypoint Detection Model,Florence-2 Model,Circle Visualization,Multi-Label Classification Model,Google Gemini,Detections Merge,Icon Visualization,Detections List Roll-Up,Camera Focus,Detection Offset,Polygon Visualization,Webhook Sink,Polygon Zone Visualization,Google Gemini,Roboflow Dataset Upload,Llama 3.2 Vision,Distance Measurement,Seg Preview,Object Detection Model,Image Slicer,Byte Tracker,Line Counter Visualization,OC-SORT Tracker,Bounding Box Visualization,Segment Anything 2 Model - outputs:
Dominant Color,Email Notification,Instance Segmentation Model,ByteTrack Tracker,Google Vision OCR,Contrast Equalization,Stability AI Image Generation,Model Comparison Visualization,Absolute Static Crop,Keypoint Visualization,Trace Visualization,SIFT,Roboflow Dataset Upload,Twilio SMS/MMS Notification,CLIP Embedding Model,Buffer,SAM 3,GLM-OCR,Reference Path Visualization,Halo Visualization,OCR Model,SIFT Comparison,VLM As Classifier,Time in Zone,Detections Stabilizer,VLM As Detector,Image Preprocessing,SORT Tracker,Perception Encoder Embedding Model,Crop Visualization,OpenAI,OpenAI,Label Visualization,Pixelate Visualization,Classification Label Visualization,Qwen3.5-VL,Corner Visualization,Stitch Images,Background Subtraction,LMM For Classification,EasyOCR,Morphological Transformation,Qwen2.5-VL,SAM 3,OpenAI,Clip Comparison,Single-Label Classification Model,Background Color Visualization,Anthropic Claude,Image Threshold,Google Gemini,Camera Calibration,Stability AI Outpainting,Halo Visualization,CogVLM,OpenAI,Single-Label Classification Model,Ellipse Visualization,Detections Stitch,VLM As Classifier,Heatmap Visualization,Image Convert Grayscale,Triangle Visualization,Semantic Segmentation Model,Image Blur,Depth Estimation,Barcode Detection,Color Visualization,Camera Focus,Text Display,Anthropic Claude,Dot Visualization,Image Slicer,SmolVLM2,SAM 3,Template Matching,Keypoint Detection Model,Polygon Visualization,Florence-2 Model,Motion Detection,Circle Visualization,Blur Visualization,Qwen3-VL,Multi-Label Classification Model,Google Gemini,LMM,Icon Visualization,Camera Focus,Stability AI Inpainting,Polygon Visualization,Polygon Zone Visualization,Florence-2 Model,Gaze Detection,Perspective Correction,Anthropic Claude,Instance Segmentation Model,Mask Visualization,Moondream2,QR Code Detection,Google Gemini,Image Contours,Clip Comparison,YOLO-World Model,Dynamic Crop,Roboflow Dataset Upload,Llama 3.2 Vision,Seg Preview,Keypoint Detection Model,Object Detection Model,Pixel Color Count,VLM As Detector,Object Detection Model,Image Slicer,Byte Tracker,Line Counter Visualization,Relative Static Crop,Multi-Label Classification Model,OC-SORT Tracker,Bounding Box Visualization,Segment Anything 2 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,rle_instance_segmentation_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..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
}