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:
- Takes an image and line zone coordinates (two points: [x1, y1] and [x2, y2]) as input
- Creates a line mask from the zone coordinates using the specified color and thickness
- Overlays the line onto the image with the specified opacity, creating a semi-transparent line visualization
- Displays text labels showing the count_in (objects that crossed into the zone) and count_out (objects that crossed out of the zone) values
- Positions the count text at the starting point of the line (x1, y1) with customizable text styling
- 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:
Triangle Visualization,Morphological Transformation,Roboflow Dataset Upload,Ellipse Visualization,LMM,Florence-2 Model,Blur Visualization,Halo Visualization,Anthropic Claude,Google Gemini,Camera Focus,Llama 3.2 Vision,Motion Detection,Model Comparison Visualization,VLM As Detector,Keypoint Visualization,Pixelate Visualization,Size Measurement,Image Slicer,SIFT Comparison,Roboflow Dataset Upload,Line Counter Visualization,Stitch OCR Detections,Label Visualization,SIFT Comparison,QR Code Generator,Line Counter,Dynamic Zone,Distance Measurement,Email Notification,Clip Comparison,Buffer,Slack Notification,Corner Visualization,Image Slicer,Florence-2 Model,CSV Formatter,EasyOCR,Object Detection Model,Anthropic Claude,OpenAI,Google Gemini,Bounding Box Visualization,Keypoint Detection Model,Anthropic Claude,Background Subtraction,Pixel Color Count,Background Color Visualization,Image Convert Grayscale,Camera Calibration,Polygon Visualization,Image Blur,VLM As Classifier,Relative Static Crop,Clip Comparison,Heatmap Visualization,CogVLM,Mask Visualization,Image Preprocessing,Twilio SMS Notification,VLM As Detector,PTZ Tracking (ONVIF).md),OpenAI,OCR Model,SIFT,Stitch Images,Stability AI Outpainting,Stitch OCR Detections,Dynamic Crop,Model Monitoring Inference Aggregator,Circle Visualization,Color Visualization,Trace Visualization,OpenAI,Dimension Collapse,Icon Visualization,Dot Visualization,Cosine Similarity,Email Notification,Instance Segmentation Model,Camera Focus,Twilio SMS/MMS Notification,Depth Estimation,Contrast Equalization,Roboflow Custom Metadata,LMM For Classification,Grid Visualization,Text Display,Reference Path Visualization,Image Threshold,Perspective Correction,Image Contours,Polygon Zone Visualization,Multi-Label Classification Model,Detection Event Log,Polygon Visualization,Local File Sink,Identify Changes,Halo Visualization,JSON Parser,Google Vision OCR,Stability AI Inpainting,Identify Outliers,Crop Visualization,Detections Consensus,Template Matching,Detections List Roll-Up,Google Gemini,Webhook Sink,Absolute Static Crop,Classification Label Visualization,VLM As Classifier,OpenAI,Single-Label Classification Model,Line Counter,Gaze Detection,Stability AI Image Generation - outputs:
Triangle Visualization,Detections Stitch,Roboflow Dataset Upload,Ellipse Visualization,Morphological Transformation,LMM,Florence-2 Model,Blur Visualization,CLIP Embedding Model,Anthropic Claude,Halo Visualization,Google Gemini,Camera Focus,Llama 3.2 Vision,Motion Detection,Model Comparison Visualization,VLM As Detector,Keypoint Visualization,Qwen3-VL,Pixelate Visualization,Image Slicer,Roboflow Dataset Upload,Line Counter Visualization,Keypoint Detection Model,SmolVLM2,Label Visualization,SIFT Comparison,Clip Comparison,Buffer,SAM 3,Detections Stabilizer,Object Detection Model,EasyOCR,Image Slicer,Corner Visualization,Florence-2 Model,Object Detection Model,SAM 3,QR Code Detection,Anthropic Claude,OpenAI,Google Gemini,Perception Encoder Embedding Model,Bounding Box Visualization,Keypoint Detection Model,Anthropic Claude,Pixel Color Count,Background Subtraction,Background Color Visualization,Image Convert Grayscale,Camera Calibration,Polygon Visualization,Image Blur,VLM As Classifier,Relative Static Crop,Clip Comparison,Heatmap Visualization,CogVLM,Mask Visualization,Image Preprocessing,VLM As Detector,Instance Segmentation Model,OpenAI,OCR Model,SIFT,Stitch Images,Single-Label Classification Model,Moondream2,Stability AI Outpainting,Dynamic Crop,Circle Visualization,Byte Tracker,OpenAI,Trace Visualization,Color Visualization,Qwen2.5-VL,Icon Visualization,YOLO-World Model,Dot Visualization,Time in Zone,Email Notification,Instance Segmentation Model,Camera Focus,Twilio SMS/MMS Notification,Contrast Equalization,Depth Estimation,Segment Anything 2 Model,LMM For Classification,Text Display,Dominant Color,Reference Path Visualization,Image Threshold,Perspective Correction,Multi-Label Classification Model,Image Contours,Polygon Zone Visualization,Polygon Visualization,Halo Visualization,Google Vision OCR,SAM 3,Stability AI Inpainting,Crop Visualization,Template Matching,Google Gemini,Barcode Detection,VLM As Classifier,Classification Label Visualization,Seg Preview,OpenAI,Absolute Static Crop,Multi-Label Classification Model,Single-Label Classification Model,Gaze Detection,Stability AI Image Generation
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
}