Pixelate Visualization

Class: PixelateVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.pixelate.v1.PixelateVisualizationBlockV1

The PixelateVisualization block pixelates detected objects in an image using Supervision's sv.PixelateAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/pixelate_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..	✅
pixel_size	int	Size of the pixelation..	✅

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 Pixelate Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Pixelate 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[rle_instance_segmentation_prediction, instance_segmentation_prediction, object_detection_prediction, keypoint_detection_prediction]): Model predictions to visualize..
  • pixel_size (integer): Size of the pixelation..

• output

  • image (image): Image in workflows.

Example JSON definition of step Pixelate Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/pixelate_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.object_detection_model.predictions",
    "pixel_size": 20
}