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:
Stitch Images,Pixelate Visualization,Path Deviation,Multi-Label Classification Model,LMM For Classification,Keypoint Detection Model,Gaze Detection,Line Counter,Instance Segmentation Model,Blur Visualization,Single-Label Classification Model,Mask Visualization,Object Detection Model,OCR Model,SIFT,Line Counter,Detections Filter,YOLO-World Model,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,Byte Tracker,Keypoint Visualization,Detections Classes Replacement,Template Matching,Image Preprocessing,Detection Offset,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,Byte Tracker,LMM,Stability AI Inpainting,Reference Path Visualization,VLM as Detector,Dynamic Crop,Byte Tracker,Triangle Visualization,Bounding Rectangle,Absolute Static Crop,Object Detection Model,Distance Measurement,Time in Zone,Detections Stitch,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,Detections Consensus,Webhook Sink,Roboflow Custom Metadata,OpenAI,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,Google Gemini,Dynamic Zone,Segment Anything 2 Model,JSON Parser,Identify Outliers,Time in Zone,Florence-2 Model,Detections Stabilizer,Path Deviation,OpenAI,Color Visualization,Pixel Color Count,CSV Formatter,Llama 3.2 Vision,Detections Transformation - 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
Crop 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..predictions(Union[instance_segmentation_prediction,keypoint_detection_prediction,object_detection_prediction]): Predictions.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
}