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

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"
}