Dynamic Zone

Class: DynamicZonesBlockV1

Source: inference.core.workflows.core_steps.transformations.dynamic_zones.v1.DynamicZonesBlockV1

The DynamicZoneBlock is a transformer block designed to simplify polygon so it's geometrically convex and then reduce number of vertices to requested amount. This block is best suited when Zone needs to be created based on shape of detected object (i.e. basketball field, road segment, zebra crossing etc.) Input detections should be filtered and contain only desired classes of interest.

Type identifier

Use the following identifier in step "type" field: roboflow_core/dynamic_zone@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
required_number_of_vertices	int	Keep simplifying polygon until number of vertices matches this number.	✅
scale_ratio	float	Expand resulting polygon along imaginary line from centroid to edge by this ratio.	✅
apply_least_squares	bool	Apply least squares algorithm to fit resulting polygon edges to base contour.	✅
midpoint_fraction	float	Fraction of vertices to keep in the middle of each edge before fitting least squares line. This parameter is useful when vertices of convex polygon are not aligned with edge that would be otherwise fitted to points closer to the center of each edge..	✅

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 Dynamic Zone in version v1.

• inputs: Detections Stitch, Gaze Detection, SIFT Comparison, Detections Classes Replacement, Detection Offset, JSON Parser, Distance Measurement, VLM as Detector, Line Counter, SIFT Comparison, Detections Filter, Identify Outliers, Dynamic Zone, Instance Segmentation Model, Detections Stabilizer, Model Monitoring Inference Aggregator, Cosine Similarity, Template Matching, Bounding Rectangle, Path Deviation, VLM as Detector, Roboflow Dataset Upload, Roboflow Dataset Upload, Time in Zone, Email Notification, Camera Focus, Path Deviation, Segment Anything 2 Model, ONVIF Control, VLM as Classifier, Line Counter, Twilio SMS Notification, Image Contours, Roboflow Custom Metadata, Clip Comparison, Time in Zone, Perspective Correction, Dynamic Crop, Slack Notification, Pixel Color Count, Detections Transformation, Instance Segmentation Model, VLM as Classifier, Webhook Sink, Velocity, Identify Changes, Detections Consensus, Local File Sink
• outputs: Keypoint Detection Model, OpenAI, Cache Set, Anthropic Claude, Gaze Detection, Detections Classes Replacement, Florence-2 Model, Detection Offset, Distance Measurement, Pixelate Visualization, Single-Label Classification Model, VLM as Detector, YOLO-World Model, Object Detection Model, Mask Visualization, Overlap Filter, Triangle Visualization, Path Deviation, VLM as Detector, Polygon Zone Visualization, Model Comparison Visualization, Crop Visualization, Classification Label Visualization, Segment Anything 2 Model, Keypoint Detection Model, Reference Path Visualization, Multi-Label Classification Model, Line Counter, Twilio SMS Notification, Google Gemini, Bounding Box Visualization, Roboflow Custom Metadata, Byte Tracker, Size Measurement, Circle Visualization, Perspective Correction, Slack Notification, Polygon Visualization, Detections Transformation, Instance Segmentation Model, VLM as Classifier, Trace Visualization, Detections Merge, Webhook Sink, Color Visualization, Detections Consensus, LMM For Classification, Detections Stitch, SIFT Comparison, Clip Comparison, Line Counter Visualization, Multi-Label Classification Model, Line Counter, Dot Visualization, Detections Filter, Dynamic Zone, Instance Segmentation Model, Florence-2 Model, Background Color Visualization, Model Monitoring Inference Aggregator, Detections Stabilizer, Template Matching, Bounding Rectangle, Roboflow Dataset Upload, Roboflow Dataset Upload, Time in Zone, Email Notification, Grid Visualization, Path Deviation, Object Detection Model, Label Visualization, Stability AI Inpainting, Blur Visualization, Llama 3.2 Vision, Byte Tracker, Ellipse Visualization, ONVIF Control, VLM as Classifier, Byte Tracker, Halo Visualization, Corner Visualization, Clip Comparison, Time in Zone, Buffer, Dynamic Crop, Single-Label Classification Model, OpenAI, Keypoint Visualization, Velocity

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Dynamic Zone in version v1 has.

Bindings

• input

  • predictions (instance_segmentation_prediction): .
  • required_number_of_vertices (integer): Keep simplifying polygon until number of vertices matches this number.
  • scale_ratio (float): Expand resulting polygon along imaginary line from centroid to edge by this ratio.
  • apply_least_squares (boolean): Apply least squares algorithm to fit resulting polygon edges to base contour.
  • midpoint_fraction (float_zero_to_one): Fraction of vertices to keep in the middle of each edge before fitting least squares line. This parameter is useful when vertices of convex polygon are not aligned with edge that would be otherwise fitted to points closer to the center of each edge..

• output

  • zones (list_of_values): List of values of any type.
  • predictions (instance_segmentation_prediction): Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object.
  • simplification_converged (boolean): Boolean flag.

Example JSON definition of step Dynamic Zone in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/dynamic_zone@v1",
    "predictions": "$segmentation.predictions",
    "required_number_of_vertices": 4,
    "scale_ratio": 1.05,
    "apply_least_squares": true,
    "midpoint_fraction": 0.9
}