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:
S3 Sink,Email Notification,Clip Comparison,Morphological Transformation,VLM As Detector,Qwen-VL,Twilio SMS/MMS Notification,Line Counter,MoonshotAI Kimi,Polygon Zone Visualization,Stitch OCR Detections,OpenAI-Compatible LLM,VLM As Detector,OpenAI,Heatmap Visualization,Keypoint Visualization,Email Notification,Llama 3.2 Vision,Anthropic Claude,Stability AI Image Generation,Google Vision OCR,Camera Focus,Label Visualization,Instance Segmentation Model,Local File Sink,Google Gemini,Motion Detection,Background Color Visualization,Qwen 3.5 API,Google Gemini,Polygon Visualization,SIFT Comparison,Grid Visualization,Florence-2 Model,Detection Event Log,OCR Model,VLM As Classifier,LMM For Classification,Keypoint Detection Model,Image Preprocessing,SIFT,Roboflow Dataset Upload,Dynamic Zone,Corner Visualization,Stability AI Outpainting,Halo Visualization,Multi-Label Classification Model,Qwen3.5-VL,Detections List Roll-Up,Blur Visualization,Distance Measurement,Morphological Transformation,Trace Visualization,VLM As Classifier,Stitch OCR Detections,Gaze Detection,Reference Path Visualization,Halo Visualization,Model Comparison Visualization,Dot Visualization,JSON Parser,Pixel Color Count,Background Subtraction,Text Display,Absolute Static Crop,CSV Formatter,Florence-2 Model,Identify Outliers,Icon Visualization,Perspective Correction,Stability AI Inpainting,Image Convert Grayscale,Line Counter,QR Code Generator,OpenRouter,Model Monitoring Inference Aggregator,OpenAI,Llama 3.2 Vision,Image Threshold,Anthropic Claude,Dynamic Crop,Detections Consensus,Size Measurement,Clip Comparison,Contrast Enhancement,Bounding Box Visualization,Depth Estimation,Image Contours,EasyOCR,Relative Static Crop,Polygon Visualization,Google Gemma API,Qwen 3.6 API,Template Matching,Image Blur,Anthropic Claude,Triangle Visualization,Object Detection Model,Roboflow Custom Metadata,SIFT Comparison,OpenAI,Slack Notification,Image Stack,Pixelate Visualization,Stitch Images,Single-Label Classification Model,OpenAI,Buffer,Image Slicer,Line Counter Visualization,Image Slicer,Cosine Similarity,LMM,Roboflow Dataset Upload,Color Visualization,Google Gemini,Classification Label Visualization,Camera Focus,Camera Calibration,Ellipse Visualization,PTZ Tracking (ONVIF),Identify Changes,Mask Visualization,GLM-OCR,Crop Visualization,Circle Visualization,CogVLM,Dimension Collapse,Contrast Equalization,Roboflow Vision Events,Webhook Sink,Twilio SMS Notification,MoonshotAI Kimi,Google Gemma - outputs:
Keypoint Detection Model,Clip Comparison,Morphological Transformation,SAM 3,Qwen-VL,VLM As Detector,Email Notification,Twilio SMS/MMS Notification,YOLO-World Model,MoonshotAI Kimi,Polygon Zone Visualization,OpenAI,VLM As Detector,Heatmap Visualization,Keypoint Visualization,Llama 3.2 Vision,Anthropic Claude,Stability AI Image Generation,Google Vision OCR,Seg Preview,Camera Focus,Label Visualization,SAM 3,Instance Segmentation Model,Qwen3.5,Multi-Label Classification Model,SmolVLM2,Google Gemini,Motion Detection,Background Color Visualization,Mask Edge Snap,Instance Segmentation Model,Qwen 3.5 API,Google Gemini,Polygon Visualization,Moondream2,SIFT Comparison,Florence-2 Model,Barcode Detection,Time in Zone,Single-Label Classification Model,OCR Model,VLM As Classifier,Qwen2.5-VL,Detections Stabilizer,LMM For Classification,Keypoint Detection Model,SIFT,Image Preprocessing,Roboflow Dataset Upload,Corner Visualization,Stability AI Outpainting,Segment Anything 2 Model,Multi-Label Classification Model,Halo Visualization,Qwen3-VL,Qwen3.5-VL,Semantic Segmentation Model,Blur Visualization,Perception Encoder Embedding Model,Morphological Transformation,Trace Visualization,VLM As Classifier,Gaze Detection,Reference Path Visualization,Halo Visualization,Model Comparison Visualization,Dot Visualization,Pixel Color Count,Background Subtraction,QR Code Detection,Text Display,ByteTrack Tracker,Absolute Static Crop,Florence-2 Model,Byte Tracker,Icon Visualization,Object Detection Model,Perspective Correction,SAM 3,BoT-SORT Tracker,Stability AI Inpainting,Image Convert Grayscale,Object Detection Model,OpenRouter,OpenAI,Llama 3.2 Vision,Image Threshold,OC-SORT Tracker,Anthropic Claude,Dynamic Crop,Clip Comparison,Dominant Color,Contrast Enhancement,Bounding Box Visualization,Depth Estimation,Keypoint Detection Model,CLIP Embedding Model,Image Contours,EasyOCR,Relative Static Crop,Multi-Label Classification Model,Polygon Visualization,Google Gemma API,Qwen 3.6 API,Template Matching,Single-Label Classification Model,Image Blur,Anthropic Claude,Triangle Visualization,Object Detection Model,OpenAI,Image Stack,Pixelate Visualization,Single-Label Classification Model,OpenAI,Instance Segmentation Model,Buffer,Stitch Images,Image Slicer,Line Counter Visualization,Image Slicer,Semantic Segmentation Model,LMM,Roboflow Dataset Upload,Color Visualization,Google Gemini,Classification Label Visualization,Camera Focus,Camera Calibration,Detections Stitch,Ellipse Visualization,SORT Tracker,Mask Visualization,GLM-OCR,Crop Visualization,Circle Visualization,CogVLM,SAM2 Video Tracker,Contrast Equalization,Roboflow Vision Events,MoonshotAI Kimi,Google Gemma
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
}