Triangle Visualization

Class: TriangleVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.triangle.v1.TriangleVisualizationBlockV1

The TriangleVisualization block draws triangle markers on an image at specific coordinates based on provided detections using Supervision's sv.TriangleAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/triangle_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..	✅
color_palette	str	Select a color palette for the visualised elements..	✅
palette_size	int	Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..	✅
custom_colors	List[str]	Define a list of custom colors for bounding boxes in HEX format..	✅
color_axis	str	Choose how bounding box colors are assigned..	✅
position	str	The anchor position for placing the triangle..	✅
base	int	Base width of the triangle in pixels..	✅
height	int	Height of the triangle in pixels..	✅
outline_thickness	int	Thickness of the outline of the triangle in pixels..	✅

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 Triangle Visualization in version v1.

• inputs: VLM as Detector, Byte Tracker, Google Vision OCR, Overlap Filter, SAM 3, Detections Stabilizer, Image Preprocessing, LMM For Classification, Ellipse Visualization, Stitch Images, Triangle Visualization, Detections Combine, QR Code Generator, Image Slicer, VLM as Classifier, Background Color Visualization, Model Monitoring Inference Aggregator, Segment Anything 2 Model, Template Matching, Distance Measurement, Dot Visualization, EasyOCR, Halo Visualization, Slack Notification, Byte Tracker, Color Visualization, JSON Parser, Llama 3.2 Vision, Line Counter, Size Measurement, Email Notification, Corner Visualization, Mask Visualization, Time in Zone, Roboflow Custom Metadata, Stability AI Outpainting, Time in Zone, Crop Visualization, VLM as Detector, Grid Visualization, Perspective Correction, Clip Comparison, Single-Label Classification Model, Contrast Equalization, Polygon Zone Visualization, Bounding Box Visualization, Camera Focus, Icon Visualization, Image Blur, Time in Zone, Path Deviation, Anthropic Claude, Dynamic Crop, Bounding Rectangle, Path Deviation, Detections Consensus, Model Comparison Visualization, Local File Sink, Identify Changes, Classification Label Visualization, Circle Visualization, SIFT Comparison, Image Contours, Relative Static Crop, Detections Filter, VLM as Classifier, Stability AI Inpainting, Moondream2, Velocity, OCR Model, Florence-2 Model, SIFT, Morphological Transformation, Detections Transformation, Reference Path Visualization, Gaze Detection, SIFT Comparison, Buffer, Polygon Visualization, Image Slicer, Florence-2 Model, Detection Offset, Clip Comparison, Image Convert Grayscale, Instance Segmentation Model, OpenAI, PTZ Tracking (ONVIF).md), Line Counter, Object Detection Model, Keypoint Detection Model, Google Gemini, Label Visualization, Email Notification, Trace Visualization, Byte Tracker, Dynamic Zone, YOLO-World Model, CogVLM, OpenAI, Detections Stitch, Stitch OCR Detections, Blur Visualization, CSV Formatter, OpenAI, Detections Classes Replacement, Twilio SMS Notification, Absolute Static Crop, Seg Preview, Roboflow Dataset Upload, Roboflow Dataset Upload, Stability AI Image Generation, Webhook Sink, Depth Estimation, Dimension Collapse, Line Counter Visualization, Instance Segmentation Model, Multi-Label Classification Model, Pixelate Visualization, Image Threshold, Detections Merge, Keypoint Detection Model, LMM, Google Gemini, Identify Outliers, Pixel Color Count, Camera Calibration, Keypoint Visualization, Object Detection Model
• outputs: VLM as Detector, Google Vision OCR, SAM 3, Classification Label Visualization, Detections Stabilizer, Circle Visualization, Image Contours, Relative Static Crop, Image Preprocessing, LMM For Classification, VLM as Classifier, Ellipse Visualization, Stitch Images, Triangle Visualization, Stability AI Inpainting, Image Slicer, VLM as Classifier, Background Color Visualization, Segment Anything 2 Model, Template Matching, Moondream2, OCR Model, Dot Visualization, Florence-2 Model, SIFT, Morphological Transformation, EasyOCR, Gaze Detection, Halo Visualization, Reference Path Visualization, SIFT Comparison, Buffer, Polygon Visualization, Image Slicer, Florence-2 Model, Clip Comparison, Perception Encoder Embedding Model, Instance Segmentation Model, OpenAI, Byte Tracker, Color Visualization, Image Convert Grayscale, Object Detection Model, Keypoint Detection Model, Google Gemini, Label Visualization, Email Notification, Llama 3.2 Vision, Trace Visualization, QR Code Detection, YOLO-World Model, Corner Visualization, Mask Visualization, Time in Zone, CogVLM, Stability AI Outpainting, OpenAI, Detections Stitch, Barcode Detection, Blur Visualization, Dominant Color, Crop Visualization, VLM as Detector, Single-Label Classification Model, OpenAI, Perspective Correction, Clip Comparison, Single-Label Classification Model, Absolute Static Crop, Seg Preview, Contrast Equalization, Roboflow Dataset Upload, Roboflow Dataset Upload, Polygon Zone Visualization, CLIP Embedding Model, Stability AI Image Generation, Depth Estimation, Bounding Box Visualization, Camera Focus, Line Counter Visualization, Instance Segmentation Model, Multi-Label Classification Model, Icon Visualization, Image Blur, Pixelate Visualization, Image Threshold, Keypoint Detection Model, Anthropic Claude, LMM, Google Gemini, Multi-Label Classification Model, Pixel Color Count, SmolVLM2, Dynamic Crop, Qwen2.5-VL, Model Comparison Visualization, Camera Calibration, Keypoint Visualization, Object Detection Model

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Triangle 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..
  • predictions (Union[object_detection_prediction, keypoint_detection_prediction, instance_segmentation_prediction]): Model predictions to visualize..
  • color_palette (string): Select a color palette for the visualised elements..
  • palette_size (integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..
  • custom_colors (list_of_values): Define a list of custom colors for bounding boxes in HEX format..
  • color_axis (string): Choose how bounding box colors are assigned..
  • position (string): The anchor position for placing the triangle..
  • base (integer): Base width of the triangle in pixels..
  • height (integer): Height of the triangle in pixels..
  • outline_thickness (integer): Thickness of the outline of the triangle in pixels..

• output

  • image (image): Image in workflows.

Example JSON definition of step Triangle Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/triangle_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.object_detection_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "position": "CENTER",
    "base": 10,
    "height": 10,
    "outline_thickness": 2
}