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