Triangle Visualization

Class: TriangleVisualizationBlockV1

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

Draw triangular markers on an image to mark specific points on detected objects, with customizable position, size, color, and outline styling, providing directional indicators and geometric markers for visual annotations.

How This Block Works

This block takes an image and detection predictions and draws triangular markers at specified anchor positions on each detected object. The block:

1. Takes an image and predictions as input
2. Determines the triangle position for each detection based on the selected anchor point (center, corners, edges, or center of mass)
3. Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
4. Draws triangular markers with the specified base width and height dimensions, with optional outline thickness using Supervision's TriangleAnnotator
5. Returns an annotated image with triangular markers overlaid on the original image

The block supports various position options including the center of the bounding box, any of the four corners, edge midpoints, or the center of mass (useful for objects with irregular shapes). Triangles can be customized with different sizes (base width and height), optional outlines for better visibility, and various color palettes. Triangular markers provide a distinctive geometric shape that can serve as directional indicators (pointing in a specific direction) or geometric markers, offering an alternative to circular dots or square markers. This provides a clean visualization style that marks detection locations with directional or geometric emphasis, making it ideal for applications requiring directional indicators, geometric markers, or distinctive point markers.

Common Use Cases

• Directional Object Marking: Mark detected objects with triangular markers that can indicate direction or orientation, useful for tracking applications, motion analysis, or directional workflows where the triangle's pointed shape provides directional information
• Geometric Marker Visualization: Use triangular markers as distinctive geometric shapes to mark detection locations, providing visual variety compared to circular dots or rectangular bounding boxes for design purposes or geometric emphasis
• Minimal Object Marking: Mark detected objects with small triangular markers instead of bounding boxes for cleaner, less cluttered visualizations when working with dense scenes or many detections, with the triangular shape providing visual distinction
• Tracking Visualization: Use triangular markers to visualize object trajectories or tracking IDs over time, creating a cleaner alternative to bounding boxes for tracking workflows, with triangles potentially indicating movement direction
• Point of Interest Highlighting: Mark specific anchor points (corners, center, center of mass) on detected objects with triangular markers for applications like object tracking, spatial analysis, or geometric annotation workflows
• Design and Aesthetic Applications: Create triangular markers for design purposes, user interfaces, dashboards, or artistic visualizations where geometric shapes provide distinctive visual style or aesthetic appeal

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Bounding Box Visualization, Label Visualization, Trace Visualization) to combine triangular markers with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save annotated images with triangular markers for documentation, reporting, or analysis
• Webhook blocks to send visualized results with triangular markers 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 triangular markers as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with triangular markers for live monitoring, tracking visualization, or post-processing analysis

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	Anchor position for placing the triangle marker relative to the detection's bounding box. Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object, useful for irregular shapes)..	✅
base	int	Base width of the triangle in pixels. Controls the horizontal width of the triangular marker at its base. Larger values create wider triangles, while smaller values create narrower triangles. Works together with height to control the overall triangle size and shape..	✅
height	int	Height of the triangle in pixels. Controls the vertical height of the triangular marker from base to tip. Larger values create taller triangles, while smaller values create shorter triangles. Works together with base to control the overall triangle size and shape..	✅
outline_thickness	int	Thickness of the triangle outline in pixels. A value of 0 creates a filled triangle with no outline. Higher values create thicker outlines around the triangle border, improving visibility and contrast. Useful for making triangular markers more visible against complex backgrounds..	✅

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

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[instance_segmentation_prediction, keypoint_detection_prediction, object_detection_prediction, rle_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): Anchor position for placing the triangle marker relative to the detection's bounding box. Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object, useful for irregular shapes)..
  • base (integer): Base width of the triangle in pixels. Controls the horizontal width of the triangular marker at its base. Larger values create wider triangles, while smaller values create narrower triangles. Works together with height to control the overall triangle size and shape..
  • height (integer): Height of the triangle in pixels. Controls the vertical height of the triangular marker from base to tip. Larger values create taller triangles, while smaller values create shorter triangles. Works together with base to control the overall triangle size and shape..
  • outline_thickness (integer): Thickness of the triangle outline in pixels. A value of 0 creates a filled triangle with no outline. Higher values create thicker outlines around the triangle border, improving visibility and contrast. Useful for making triangular markers more visible against complex backgrounds..

• 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
}