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

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
}