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:
Icon Visualization,Roboflow Dataset Upload,Slack Notification,Label Visualization,Instance Segmentation Model,Object Detection Model,Dot Visualization,Camera Calibration,Polygon Zone Visualization,SIFT,Trace Visualization,Morphological Transformation,Perspective Correction,Roboflow Custom Metadata,Dynamic Zone,Florence-2 Model,Anthropic Claude,Relative Static Crop,Image Slicer,Image Threshold,Keypoint Visualization,PTZ Tracking (ONVIF),Template Matching,Absolute Static Crop,Blur Visualization,Circle Visualization,Email Notification,Camera Focus,Crop Visualization,Email Notification,Classification Label Visualization,OpenAI,EasyOCR,Google Gemini,OpenAI,Gaze Detection,Polygon Visualization,Identify Outliers,OpenAI,VLM As Classifier,Stitch OCR Detections,Color Visualization,Local File Sink,Twilio SMS Notification,Line Counter,Twilio SMS/MMS Notification,CSV Formatter,Image Contours,Model Monitoring Inference Aggregator,Keypoint Detection Model,Roboflow Vision Events,Anthropic Claude,OpenAI,Google Gemini,Image Convert Grayscale,Llama 3.2 Vision,Distance Measurement,VLM As Detector,Clip Comparison,Cosine Similarity,JSON Parser,Triangle Visualization,Stability AI Inpainting,LMM For Classification,Background Color Visualization,Identify Changes,Stitch OCR Detections,Pixel Color Count,Depth Estimation,S3 Sink,Line Counter,Model Comparison Visualization,Qwen3.5-VL,CogVLM,Image Blur,Stitch Images,Anthropic Claude,Motion Detection,Contrast Equalization,Google Gemini,Corner Visualization,Detections List Roll-Up,Halo Visualization,Stability AI Image Generation,Florence-2 Model,Detections Consensus,Reference Path Visualization,Size Measurement,Buffer,LMM,Dynamic Crop,Line Counter Visualization,Clip Comparison,SIFT Comparison,Roboflow Dataset Upload,Heatmap Visualization,Text Display,VLM As Classifier,VLM As Detector,Multi-Label Classification Model,Grid Visualization,Polygon Visualization,Camera Focus,Single-Label Classification Model,Webhook Sink,Image Slicer,Image Preprocessing,SIFT Comparison,Bounding Box Visualization,Stability AI Outpainting,Halo Visualization,OCR Model,Detection Event Log,Background Subtraction,Dimension Collapse,GLM-OCR,QR Code Generator,Pixelate Visualization,Ellipse Visualization,Google Vision OCR,Mask Visualization - outputs:
Icon Visualization,Moondream2,Instance Segmentation Model,Label Visualization,Multi-Label Classification Model,Dot Visualization,Camera Calibration,Trace Visualization,SAM 3,Time in Zone,Semantic Segmentation Model,Relative Static Crop,Image Threshold,Keypoint Visualization,Template Matching,Single-Label Classification Model,Blur Visualization,Circle Visualization,Keypoint Detection Model,Crop Visualization,Email Notification,Classification Label Visualization,OpenAI,Google Gemini,OpenAI,YOLO-World Model,Twilio SMS/MMS Notification,Keypoint Detection Model,Anthropic Claude,Google Gemini,Image Convert Grayscale,Stability AI Inpainting,Depth Estimation,Model Comparison Visualization,SAM2 Video Tracker,Motion Detection,Google Gemini,CLIP Embedding Model,Florence-2 Model,LMM,Dynamic Crop,SAM 3,Barcode Detection,Clip Comparison,Perception Encoder Embedding Model,Detections Stabilizer,Byte Tracker,VLM As Detector,Multi-Label Classification Model,Polygon Visualization,Mask Visualization,Semantic Segmentation Model,Seg Preview,Image Slicer,SORT Tracker,OC-SORT Tracker,OCR Model,Qwen3-VL,Object Detection Model,Object Detection Model,GLM-OCR,Roboflow Dataset Upload,Object Detection Model,Polygon Zone Visualization,Detections Stitch,SIFT,Morphological Transformation,Instance Segmentation Model,Perspective Correction,Anthropic Claude,Image Slicer,Absolute Static Crop,SmolVLM2,EasyOCR,Camera Focus,SAM 3,Gaze Detection,Polygon Visualization,OpenAI,VLM As Classifier,Color Visualization,Image Contours,Roboflow Vision Events,OpenAI,Single-Label Classification Model,Llama 3.2 Vision,VLM As Detector,Instance Segmentation Model,Clip Comparison,LMM For Classification,Triangle Visualization,Pixel Color Count,Background Color Visualization,Qwen3.5-VL,CogVLM,Qwen2.5-VL,Image Blur,Anthropic Claude,Stitch Images,Dominant Color,Contrast Equalization,Corner Visualization,Stability AI Image Generation,Halo Visualization,Reference Path Visualization,QR Code Detection,Buffer,Line Counter Visualization,ByteTrack Tracker,Multi-Label Classification Model,Keypoint Detection Model,Roboflow Dataset Upload,Heatmap Visualization,Text Display,VLM As Classifier,Segment Anything 2 Model,Camera Focus,Single-Label Classification Model,Image Preprocessing,SIFT Comparison,Stability AI Outpainting,Bounding Box Visualization,Halo Visualization,Background Subtraction,Pixelate Visualization,Google Vision OCR,Ellipse Visualization,Florence-2 Model
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
}