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

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
}