Line Counter Visualization

Class: LineCounterZoneVisualizationBlockV1

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

Draw a line zone on an image to visualize counting boundaries, displaying a colored line overlay with in/out count labels for line counter workflows that track objects crossing a specified line.

How This Block Works

This block takes an image and line zone coordinates (two points defining a line) and draws a visual representation of the counting line with count statistics. The block:

1. Takes an image and line zone coordinates (two points: [x1, y1] and [x2, y2]) as input
2. Creates a line mask from the zone coordinates using the specified color and thickness
3. Overlays the line onto the image with the specified opacity, creating a semi-transparent line visualization
4. Displays text labels showing the count_in (objects that crossed into the zone) and count_out (objects that crossed out of the zone) values
5. Positions the count text at the starting point of the line (x1, y1) with customizable text styling
6. Returns an annotated image with the line zone and count statistics overlaid on the original image

The block visualizes line counting zones used to track object movement across a defined boundary line. The line is drawn between the two specified points with customizable color, thickness, and opacity. Count statistics (in and out) are displayed as text labels, typically connected from a Line Counter block that tracks object crossings. The visualization helps users see the counting boundary and monitor counting results in real-time. Note: This block should typically be placed before other visualization blocks in the workflow, as the line zone provides a background reference layer for object detection visualizations.

Common Use Cases

• Line Counter Visualization: Visualize line counting zones for people counting, vehicle counting, or object tracking workflows where objects cross a defined line boundary, displaying the counting line and in/out statistics
• Traffic and Movement Monitoring: Display counting lines for traffic monitoring, pedestrian flow analysis, or entry/exit tracking applications where you need to visualize the counting boundary and current counts
• Checkpoint and Access Control: Visualize counting lines at checkpoints, gates, or access points to show the monitoring boundary and track entry/exit counts for security or access control workflows
• Retail and Business Analytics: Display counting lines for foot traffic analysis, customer flow monitoring, or occupancy tracking in retail, hospitality, or business intelligence applications
• Crowd Management and Safety: Visualize counting lines for crowd management, capacity monitoring, or safety workflows where tracking object movement across boundaries is critical
• Real-Time Counting Dashboards: Create visual overlays for real-time counting dashboards, monitoring interfaces, or live video feeds where the counting line and statistics need to be clearly visible

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Line Counter blocks to receive count_in and count_out values that are displayed on the visualization
• Other visualization blocks (e.g., Bounding Box Visualization, Label Visualization, Polygon Visualization) to add object detection annotations on top of the line zone visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with line zone visualizations for documentation, reporting, or analysis
• Webhook blocks to send visualized results with line zones to external systems, APIs, or web applications for display in dashboards or monitoring tools
• Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with line zones as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with line zone visualizations for live monitoring, counting visualization, or post-processing analysis

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 zone coordinates in the format [[x1, y1], [x2, y2]] consisting of exactly two points that define the counting line. The line is drawn between these two points, and objects crossing this line are counted. Typically connected from a Line Counter block's zone output..	✅
color	str	Color of the line zone. Can be specified as a color name (e.g., 'WHITE', 'RED'), hex color code (e.g., '#5bb573', '#FFFFFF'), or RGB format (e.g., 'rgb(255, 255, 255)'). The line is drawn in this color with the specified opacity..	✅
thickness	int	Thickness of the line zone in pixels. Controls how thick the counting line appears. Higher values create thicker, more visible lines, while lower values create thinner lines. Typical values range from 1 to 10 pixels..	✅
text_thickness	int	Thickness of the count text labels in pixels. Controls how bold the text appears (line width of text characters). Higher values create thicker, bolder text, while lower values create thinner text. Typical values range from 1 to 3..	✅
text_scale	float	Scale factor for the count text labels. Controls the size of the text displaying count_in and count_out values. Values greater than 1.0 make text larger, values less than 1.0 make text smaller. Typical values range from 0.5 to 2.0..	✅
count_in	int	Number of objects that crossed into the line zone (crossing from one side to the other in the 'in' direction). Typically connected from a Line Counter block's count_in output (e.g., '$steps.line_counter.count_in'). This value is displayed in the visualization text label..	✅
count_out	int	Number of objects that crossed out of the line zone (crossing from one side to the other in the 'out' direction). Typically connected from a Line Counter block's count_out output (e.g., '$steps.line_counter.count_out'). This value is displayed in the visualization text label..	✅
opacity	float	Opacity of the line zone overlay, ranging from 0.0 (fully transparent) to 1.0 (fully opaque). Controls how transparent the counting line appears over the image. Lower values create more transparent lines that blend with the background, while higher values create more opaque, visible lines. Typical values range from 0.2 to 0.5 for balanced visibility..	✅

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: Contrast Equalization, Llama 3.2 Vision, Clip Comparison, SIFT Comparison, Anthropic Claude, VLM as Detector, Local File Sink, Polygon Visualization, QR Code Generator, Image Blur, SIFT Comparison, Email Notification, Roboflow Dataset Upload, Text Display, Motion Detection, Model Comparison Visualization, Camera Focus, SIFT, PTZ Tracking (ONVIF).md), LMM, Google Vision OCR, Mask Visualization, Anthropic Claude, Relative Static Crop, Cosine Similarity, Circle Visualization, EasyOCR, Pixelate Visualization, Stability AI Inpainting, Reference Path Visualization, VLM as Classifier, Instance Segmentation Model, Perspective Correction, Ellipse Visualization, Crop Visualization, Halo Visualization, Image Threshold, Keypoint Detection Model, CSV Formatter, Florence-2 Model, Twilio SMS Notification, Image Convert Grayscale, Corner Visualization, Image Preprocessing, Line Counter, Dynamic Zone, Detections List Roll-Up, Identify Changes, Icon Visualization, Background Subtraction, Image Contours, Image Slicer, Detections Consensus, Depth Estimation, Multi-Label Classification Model, Pixel Color Count, Stitch Images, Dynamic Crop, Bounding Box Visualization, VLM as Classifier, Model Monitoring Inference Aggregator, Detection Event Log, Line Counter Visualization, Blur Visualization, Morphological Transformation, Camera Calibration, Polygon Zone Visualization, Single-Label Classification Model, Email Notification, Line Counter, Stability AI Image Generation, Keypoint Visualization, OCR Model, Roboflow Custom Metadata, Google Gemini, OpenAI, Distance Measurement, Camera Focus, Trace Visualization, OpenAI, CogVLM, Color Visualization, Absolute Static Crop, Image Slicer, Size Measurement, Dot Visualization, Identify Outliers, Label Visualization, Slack Notification, Buffer, Florence-2 Model, Google Gemini, JSON Parser, Google Gemini, Grid Visualization, Object Detection Model, LMM For Classification, OpenAI, Stitch OCR Detections, Template Matching, Dimension Collapse, Gaze Detection, OpenAI, Classification Label Visualization, Background Color Visualization, Stability AI Outpainting, Roboflow Dataset Upload, Stitch OCR Detections, Twilio SMS/MMS Notification, Anthropic Claude, Clip Comparison, Triangle Visualization, VLM as Detector, Webhook Sink
• outputs: Contrast Equalization, Llama 3.2 Vision, Clip Comparison, Anthropic Claude, VLM as Detector, Polygon Visualization, Image Blur, SIFT Comparison, SmolVLM2, CLIP Embedding Model, Roboflow Dataset Upload, Text Display, Motion Detection, SIFT, Model Comparison Visualization, Camera Focus, Moondream2, LMM, Qwen3-VL, Single-Label Classification Model, Google Vision OCR, SAM 3, Anthropic Claude, Relative Static Crop, Mask Visualization, Object Detection Model, Keypoint Detection Model, Circle Visualization, Seg Preview, EasyOCR, Pixelate Visualization, Stability AI Inpainting, Multi-Label Classification Model, Time in Zone, VLM as Classifier, Reference Path Visualization, Instance Segmentation Model, Perspective Correction, Halo Visualization, Image Threshold, Ellipse Visualization, Crop Visualization, Keypoint Detection Model, Florence-2 Model, Detections Stabilizer, Image Convert Grayscale, Perception Encoder Embedding Model, Corner Visualization, Image Preprocessing, Barcode Detection, Icon Visualization, SAM 3, Background Subtraction, Segment Anything 2 Model, Qwen2.5-VL, Image Slicer, Image Contours, Depth Estimation, Multi-Label Classification Model, Pixel Color Count, Detections Stitch, Stitch Images, QR Code Detection, Dynamic Crop, Bounding Box Visualization, Anthropic Claude, VLM as Classifier, YOLO-World Model, Instance Segmentation Model, Line Counter Visualization, Blur Visualization, Morphological Transformation, Camera Calibration, Polygon Zone Visualization, Single-Label Classification Model, Email Notification, Stability AI Image Generation, Dominant Color, OCR Model, Keypoint Visualization, Google Gemini, OpenAI, Camera Focus, Trace Visualization, CogVLM, OpenAI, Image Slicer, Absolute Static Crop, Color Visualization, Dot Visualization, Label Visualization, Buffer, Florence-2 Model, Google Gemini, Google Gemini, Object Detection Model, LMM For Classification, Template Matching, OpenAI, OpenAI, Classification Label Visualization, Background Color Visualization, Stability AI Outpainting, Byte Tracker, SAM 3, Twilio SMS/MMS Notification, Roboflow Dataset Upload, Gaze Detection, Clip Comparison, Triangle Visualization, VLM as Detector

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 zone coordinates in the format [[x1, y1], [x2, y2]] consisting of exactly two points that define the counting line. The line is drawn between these two points, and objects crossing this line are counted. Typically connected from a Line Counter block's zone output..
  • color (string): Color of the line zone. Can be specified as a color name (e.g., 'WHITE', 'RED'), hex color code (e.g., '#5bb573', '#FFFFFF'), or RGB format (e.g., 'rgb(255, 255, 255)'). The line is drawn in this color with the specified opacity..
  • thickness (integer): Thickness of the line zone in pixels. Controls how thick the counting line appears. Higher values create thicker, more visible lines, while lower values create thinner lines. Typical values range from 1 to 10 pixels..
  • text_thickness (integer): Thickness of the count text labels in pixels. Controls how bold the text appears (line width of text characters). Higher values create thicker, bolder text, while lower values create thinner text. Typical values range from 1 to 3..
  • text_scale (float): Scale factor for the count text labels. Controls the size of the text displaying count_in and count_out values. Values greater than 1.0 make text larger, values less than 1.0 make text smaller. Typical values range from 0.5 to 2.0..
  • count_in (integer): Number of objects that crossed into the line zone (crossing from one side to the other in the 'in' direction). Typically connected from a Line Counter block's count_in output (e.g., '$steps.line_counter.count_in'). This value is displayed in the visualization text label..
  • count_out (integer): Number of objects that crossed out of the line zone (crossing from one side to the other in the 'out' direction). Typically connected from a Line Counter block's count_out output (e.g., '$steps.line_counter.count_out'). This value is displayed in the visualization text label..
  • opacity (float_zero_to_one): Opacity of the line zone overlay, ranging from 0.0 (fully transparent) to 1.0 (fully opaque). Controls how transparent the counting line appears over the image. Lower values create more transparent lines that blend with the background, while higher values create more opaque, visible lines. Typical values range from 0.2 to 0.5 for balanced visibility..

• 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
}