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@v1
to 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:
VLM as Detector
,OpenAI
,Keypoint Detection Model
,Circle Visualization
,Roboflow Dataset Upload
,Gaze Detection
,PTZ Tracking (ONVIF)
.md),Roboflow Custom Metadata
,Depth Estimation
,SIFT
,Florence-2 Model
,Buffer
,Template Matching
,Dimension Collapse
,Grid Visualization
,Dynamic Zone
,Instance Segmentation Model
,Color Visualization
,CSV Formatter
,Detections Consensus
,Identify Outliers
,Perspective Correction
,Model Monitoring Inference Aggregator
,Image Slicer
,OpenAI
,Keypoint Detection Model
,Model Comparison Visualization
,Clip Comparison
,Stitch Images
,Dynamic Crop
,Image Contours
,Webhook Sink
,Pixelate Visualization
,Llama 3.2 Vision
,SIFT Comparison
,Camera Calibration
,Line Counter
,Reference Path Visualization
,Image Blur
,Local File Sink
,Blur Visualization
,OCR Model
,Ellipse Visualization
,Trace Visualization
,Corner Visualization
,Camera Focus
,Polygon Zone Visualization
,Detections Classes Replacement
,Google Gemini
,OpenAI
,Triangle Visualization
,Stability AI Inpainting
,Classification Label Visualization
,Detections Transformation
,Single-Label Classification Model
,Bounding Box Visualization
,Size Measurement
,Distance Measurement
,CogVLM
,Image Convert Grayscale
,Halo Visualization
,LMM
,Email Notification
,Polygon Visualization
,Absolute Static Crop
,Slack Notification
,Object Detection Model
,Dot Visualization
,Label Visualization
,Stability AI Outpainting
,Crop Visualization
,Cosine Similarity
,Detections Filter
,Google Vision OCR
,Stability AI Image Generation
,Detection Offset
,Image Threshold
,Pixel Color Count
,Stitch OCR Detections
,Image Preprocessing
,VLM as Classifier
,Identify Changes
,SIFT Comparison
,JSON Parser
,Mask Visualization
,Twilio SMS Notification
,Florence-2 Model
,Clip Comparison
,Roboflow Dataset Upload
,Line Counter Visualization
,VLM as Classifier
,Anthropic Claude
,Background Color Visualization
,LMM For Classification
,Line Counter
,Image Slicer
,Keypoint Visualization
,Multi-Label Classification Model
,VLM as Detector
,Relative Static Crop
- outputs:
YOLO-World Model
,OpenAI
,VLM as Detector
,Keypoint Detection Model
,Circle Visualization
,Gaze Detection
,Roboflow Dataset Upload
,Perception Encoder Embedding Model
,Depth Estimation
,SIFT
,Florence-2 Model
,Buffer
,Single-Label Classification Model
,Template Matching
,Detections Stitch
,Instance Segmentation Model
,Color Visualization
,Object Detection Model
,Perspective Correction
,Image Slicer
,OpenAI
,Keypoint Detection Model
,Model Comparison Visualization
,Clip Comparison
,Stitch Images
,Dynamic Crop
,Moondream2
,Image Contours
,Pixelate Visualization
,Llama 3.2 Vision
,Byte Tracker
,Camera Calibration
,Reference Path Visualization
,Time in Zone
,Image Blur
,CLIP Embedding Model
,Blur Visualization
,OCR Model
,Ellipse Visualization
,Trace Visualization
,SmolVLM2
,Polygon Zone Visualization
,Corner Visualization
,Google Gemini
,Camera Focus
,OpenAI
,Triangle Visualization
,Stability AI Inpainting
,Classification Label Visualization
,Single-Label Classification Model
,Barcode Detection
,Bounding Box Visualization
,Detections Stabilizer
,CogVLM
,Image Convert Grayscale
,Halo Visualization
,LMM
,Polygon Visualization
,Absolute Static Crop
,Object Detection Model
,Dot Visualization
,Label Visualization
,Stability AI Outpainting
,Crop Visualization
,Google Vision OCR
,Stability AI Image Generation
,Pixel Color Count
,Image Threshold
,Image Preprocessing
,VLM as Classifier
,SIFT Comparison
,Mask Visualization
,Dominant Color
,Florence-2 Model
,Segment Anything 2 Model
,Clip Comparison
,Roboflow Dataset Upload
,QR Code Detection
,Line Counter Visualization
,VLM as Classifier
,Instance Segmentation Model
,Background Color Visualization
,Anthropic Claude
,LMM For Classification
,Multi-Label Classification Model
,Image Slicer
,Keypoint Visualization
,Qwen2.5-VL
,Multi-Label Classification Model
,VLM as Detector
,Relative Static Crop
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"
}