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