Keypoint Visualization¶

Visualize keypoints (landmark points) detected on objects by drawing point markers, connecting edges, or labeled vertices, providing pose estimation visualization for anatomical points, structural landmarks, or object key features.

How This Block Works¶

This block takes an image and keypoint detection predictions and visualizes the detected keypoints using one of three visualization modes. The block:

1. Takes an image and keypoint detection predictions as input (predictions must include keypoint coordinates, confidence scores, and class names)
2. Extracts keypoint data (coordinates, confidence values, and class names) from the predictions
3. Converts the detection data into a KeyPoints format suitable for visualization
4. Applies one of three visualization modes based on the annotator_type setting:
5. Edge mode: Draws connecting lines (edges) between keypoints using specified edge pairs to show keypoint relationships (e.g., skeleton connections in pose estimation)
6. Vertex mode: Draws circular markers at each keypoint location without connections, showing individual keypoint positions
7. Vertex label mode: Draws circular markers with text labels identifying each keypoint class name, providing labeled keypoint visualization
8. Applies color styling, sizing, and optional text labeling based on the selected parameters
9. Returns an annotated image with keypoints visualized according to the selected mode

The block supports three visualization styles to suit different use cases. Edge mode connects related keypoints with lines (useful for pose estimation skeletons or structural relationships), vertex mode shows individual keypoint locations as circular markers, and vertex label mode adds text labels to identify each keypoint type. This visualization is essential for pose estimation workflows, anatomical point detection, or any application where specific landmark points on objects need to be identified and visualized.

Common Use Cases¶

• Human Pose Estimation: Visualize human body keypoints (joints, body parts) for pose estimation, activity recognition, or motion analysis applications where anatomical points need to be displayed with skeleton connections or labeled markers
• Animal Pose Estimation: Display animal keypoints for behavior analysis, veterinary applications, or wildlife monitoring where anatomical landmarks need to be visualized for pose analysis or movement tracking
• Structural Landmark Detection: Visualize keypoints on objects, structures, or machinery for structural analysis, quality control, or measurement workflows where specific landmark points need to be identified and displayed
• Facial Landmark Detection: Display facial keypoints (eye corners, nose tip, mouth corners, etc.) for facial recognition, expression analysis, or face alignment applications where facial features need to be visualized
• Sports and Movement Analysis: Visualize keypoints for sports analysis, biomechanics, or movement studies where body positions, joint angles, or movement patterns need to be analyzed and displayed
• Quality Control and Inspection: Display keypoints for manufacturing, quality assurance, or inspection workflows where specific points on products or components need to be identified, measured, or validated

Connecting to Other Blocks¶

The annotated image from this block can be connected to:

• Keypoint Detection Model blocks to receive keypoint predictions that are visualized with point markers, edges, or labeled vertices
• Other visualization blocks (e.g., Bounding Box Visualization, Label Visualization, Polygon Visualization) to combine keypoint visualization with additional annotations for comprehensive pose or structure visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with keypoint visualizations for documentation, reporting, or analysis
• Webhook blocks to send visualized results with keypoints to external systems, APIs, or web applications for display in dashboards, pose analysis tools, or monitoring interfaces
• Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with keypoints as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with keypoint visualizations for live pose estimation, movement analysis, or post-processing workflows

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¶

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections¶

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Keypoint Visualization in version v1 has.

{
    "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"
}