Bounding Box Visualization¶
Version v1¶
The BoundingBoxVisualization block draws a box around detected
objects in an image using Supervision's sv.RoundBoxAnnotator.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/bounding_box_visualization@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
The unique name of this step.. | ❌ |
copy_image |
bool |
Duplicate the image contents (vs overwriting the image in place). Deselect for chained visualizations that should stack on previous ones where the intermediate state is not needed.. | ✅ |
color_palette |
str |
Color palette to use for annotations.. | ✅ |
palette_size |
int |
Number of colors in the color palette. Applies when using a matplotlib color_palette.. |
✅ |
custom_colors |
List[str] |
List of colors to use for annotations when color_palette is set to "CUSTOM".. |
✅ |
color_axis |
str |
Strategy to use for mapping colors to annotations.. | ✅ |
thickness |
int |
Thickness of the bounding box in pixels.. | ✅ |
roundness |
float |
Roundness of the corners of the bounding box.. | ✅ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow runtime. See Bindings for more info.
Available Connections¶
Check what blocks you can connect to Bounding Box Visualization in version v1.
- inputs:
Detection Offset,Template Matching,Detections Stitch,Segment Anything 2 Model,Blur Visualization,Image Convert Grayscale,Camera Focus,Dot Visualization,VLM as Detector,Corner Visualization,Circle Visualization,Triangle Visualization,Relative Static Crop,Absolute Static Crop,Background Color Visualization,YOLO-World Model,Detections Transformation,Pixelate Visualization,Polygon Visualization,Image Threshold,Detections Consensus,Label Visualization,Crop Visualization,Mask Visualization,Detections Classes Replacement,Image Contours,Bounding Box Visualization,Object Detection Model,Color Visualization,Perspective Correction,Keypoint Detection Model,Detections Filter,Instance Segmentation Model,Halo Visualization,SIFT,Dynamic Crop,Image Blur,Ellipse Visualization,Image Slicer - outputs:
Template Matching,Roboflow Dataset Upload,Detections Stitch,Multi-Label Classification Model,Blur Visualization,Segment Anything 2 Model,Barcode Detection,Image Convert Grayscale,Camera Focus,Dot Visualization,VLM as Detector,Corner Visualization,Circle Visualization,OpenAI,Triangle Visualization,CogVLM,Relative Static Crop,Absolute Static Crop,Pixel Color Count,LMM For Classification,Background Color Visualization,YOLO-World Model,Pixelate Visualization,Polygon Visualization,Image Threshold,Label Visualization,OCR Model,Crop Visualization,Mask Visualization,Image Contours,LMM,Bounding Box Visualization,QR Code Detection,Object Detection Model,Color Visualization,Image Slicer,Perspective Correction,OpenAI,Clip Comparison,VLM as Classifier,Keypoint Detection Model,Clip Comparison,Anthropic Claude,Instance Segmentation Model,Halo Visualization,Google Gemini,Roboflow Dataset Upload,Dominant Color,SIFT,Dynamic Crop,Image Blur,Ellipse Visualization,Single-Label Classification Model
The available connections depend on its binding kinds. Check what binding kinds
Bounding Box Visualization in version v1 has.
Bindings
-
input
predictions(Union[keypoint_detection_prediction,object_detection_prediction,instance_segmentation_prediction]): Predictions.image(image): The input image for this step..copy_image(boolean): Duplicate the image contents (vs overwriting the image in place). Deselect for chained visualizations that should stack on previous ones where the intermediate state is not needed..color_palette(string): Color palette to use for annotations..palette_size(integer): Number of colors in the color palette. Applies when using a matplotlibcolor_palette..custom_colors(list_of_values): List of colors to use for annotations whencolor_paletteis set to "CUSTOM"..color_axis(string): Strategy to use for mapping colors to annotations..thickness(integer): Thickness of the bounding box in pixels..roundness(float_zero_to_one): Roundness of the corners of the bounding box..
-
output
image(image): Image in workflows.
Example JSON definition of step Bounding Box Visualization in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/bounding_box_visualization@v1",
"predictions": "$steps.object_detection_model.predictions",
"image": "$inputs.image",
"copy_image": true,
"color_palette": "DEFAULT",
"palette_size": 10,
"custom_colors": [
"#FF0000",
"#00FF00",
"#0000FF"
],
"color_axis": "CLASS",
"thickness": 2,
"roundness": 0.0
}