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