Keypoint Visualization

Class: KeypointVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.keypoint.v1.KeypointVisualizationBlockV1

The KeypointVisualization block uses a detections from an keypoint detection model to draw keypoints on objects using sv.VertexAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/keypoint_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..	✅
annotator_type	str	Type of annotator to be used for keypoint visualization..	❌
color	str	Color of the keypoint..	✅
text_color	str	Text color of the keypoint..	✅
text_scale	float	Scale of the text..	✅
text_thickness	int	Thickness of the text characters..	✅
text_padding	int	Padding around the text in pixels..	✅
thickness	int	Thickness of the outline in pixels..	✅
radius	int	Radius of the keypoint in pixels..	✅
edges	List[Any]	Mapping of keypoints to edges. List of pairs of indices..	✅

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 Keypoint Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Keypoint 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 (keypoint_detection_prediction): Predictions.
  • color (string): Color of the keypoint..
  • text_color (string): Text color of the keypoint..
  • text_scale (float): Scale of the text..
  • text_thickness (integer): Thickness of the text characters..
  • text_padding (integer): Padding around the text in pixels..
  • thickness (integer): Thickness of the outline in pixels..
  • radius (integer): Radius of the keypoint in pixels..
  • edges (list_of_values): Mapping of keypoints to edges. List of pairs of indices..

• output

  • image (image): Image in workflows.

Example JSON definition of step Keypoint Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/keypoint_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.keypoint_detection_model.predictions",
    "annotator_type": "<block_does_not_provide_example>",
    "color": "#A351FB",
    "text_color": "black",
    "text_scale": 0.5,
    "text_thickness": 1,
    "text_padding": 10,
    "thickness": 2,
    "radius": 10,
    "edges": "$inputs.edges"
}