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

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
}