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