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

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"
}