Line Counter Visualization

Class: LineCounterZoneVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.line_zone.v1.LineCounterZoneVisualizationBlockV1

The LineCounterZoneVisualization block draws line in an image with a specified color and opacity. Please note: line zone will be drawn on top of image passed to this block, this block should be placed before other visualization blocks in the workflow.

Type identifier

Use the following identifier in step "type" field: roboflow_core/line_counter_visualization@v1to 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..	✅
zone	List[Any]	Line in the format [[x1, y1], [x2, y2]] consisting of exactly two points..	✅
color	str	Color of the zone..	✅
thickness	int	Thickness of the lines in pixels..	✅
text_thickness	int	Thickness of the text in pixels..	✅
text_scale	float	Scale of the text..	✅
count_in	int	Reference to the number of objects that crossed into the line zone..	✅
count_out	int	Reference to the number of objects that crossed out of the line zone..	✅
opacity	float	Transparency of the Mask overlay..	✅

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 Line Counter Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Line Counter 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..
  • zone (list_of_values): Line in the format [[x1, y1], [x2, y2]] consisting of exactly two points..
  • color (string): Color of the zone..
  • thickness (integer): Thickness of the lines in pixels..
  • text_thickness (integer): Thickness of the text in pixels..
  • text_scale (float): Scale of the text..
  • count_in (integer): Reference to the number of objects that crossed into the line zone..
  • count_out (integer): Reference to the number of objects that crossed out of the line zone..
  • opacity (float_zero_to_one): Transparency of the Mask overlay..

• output

  • image (image): Image in workflows.

Example JSON definition of step Line Counter Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/line_counter_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "zone": [
        [
            0,
            50
        ],
        [
            500,
            50
        ]
    ],
    "color": "WHITE",
    "thickness": 2,
    "text_thickness": 1,
    "text_scale": 1.0,
    "count_in": "$steps.line_counter.count_in",
    "count_out": "$steps.line_counter.count_out",
    "opacity": 0.3
}