Color Visualization

Class: ColorVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.color.v1.ColorVisualizationBlockV1

The ColorVisualization block paints a solid color on detected objects in an image using Supervision's sv.ColorAnnotator.

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	Transparency of the color overlay..	✅

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

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, keypoint_detection_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): Transparency of the color overlay..

• 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
}