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