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

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
}