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