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