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

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
}