Crop Visualization

Class: CropVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.crop.v1.CropVisualizationBlockV1

The CropVisualization block draws scaled up crops of detections on the scene using Supervision's sv.CropAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/crop_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 crop..	✅
scale_factor	float	The factor by which to scale the cropped image part. A factor of 2, for example, would double the size of the cropped area, allowing for a closer view of the detection..	✅
border_thickness	int	Thickness of the outline 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 Crop Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Crop 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[keypoint_detection_prediction, object_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 crop..
  • scale_factor (float): The factor by which to scale the cropped image part. A factor of 2, for example, would double the size of the cropped area, allowing for a closer view of the detection..
  • border_thickness (integer): Thickness of the outline in pixels..

• output

  • image (image): Image in workflows.

Example JSON definition of step Crop Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/crop_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",
    "scale_factor": 2.0,
    "border_thickness": 2
}