Crop Visualization¶
Version v1¶
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 |
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.. | ✅ |
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¶
Check what blocks you can connect to Crop Visualization in version v1.
- inputs:
Color Visualization,Detections Filter,Detections Consensus,Image Contours,Path deviation,Image Preprocessing,Mask Visualization,Dot Visualization,Corner Visualization,Model Comparison Visualization,Image Slicer,Byte Tracker,Detection Offset,Keypoint Detection Model,Image Blur,Label Visualization,Time in zone,Relative Static Crop,Polygon Visualization,Camera Focus,Detections Classes Replacement,Dynamic Crop,YOLO-World Model,Halo Visualization,Segment Anything 2 Model,Crop Visualization,Image Threshold,SIFT,Google Vision OCR,Circle Visualization,SIFT Comparison,VLM as Detector,Path deviation,Byte Tracker,Object Detection Model,Stability AI Inpainting,Image Convert Grayscale,Line Counter Visualization,Absolute Static Crop,Background Color Visualization,Perspective Correction,Detections Transformation,Detections Stitch,Bounding Box Visualization,Polygon Zone Visualization,Template Matching,Ellipse Visualization,Pixelate Visualization,Time in zone,Triangle Visualization,Instance Segmentation Model,Bounding Rectangle,Stitch Images,Blur Visualization - outputs:
Color Visualization,Image Contours,Image Preprocessing,Mask Visualization,Dot Visualization,Corner Visualization,Model Comparison Visualization,Image Slicer,Keypoint Detection Model,Image Blur,OCR Model,Label Visualization,Time in zone,Relative Static Crop,Florence-2 Model,Camera Focus,Clip Comparison,Polygon Visualization,Image Convert Grayscale,Dynamic Crop,YOLO-World Model,Halo Visualization,LMM For Classification,Segment Anything 2 Model,Crop Visualization,Image Threshold,Multi-Label Classification Model,QR Code Detection,Google Vision OCR,SIFT,Clip Comparison,LMM,Roboflow Dataset Upload,Barcode Detection,OpenAI,Circle Visualization,SIFT Comparison,VLM as Detector,Google Gemini,CogVLM,Anthropic Claude,Object Detection Model,Pixel Color Count,Stability AI Inpainting,OpenAI,Absolute Static Crop,Background Color Visualization,Perspective Correction,Detections Stitch,Line Counter Visualization,Dominant Color,Template Matching,Bounding Box Visualization,Polygon Zone Visualization,Ellipse Visualization,Pixelate Visualization,Triangle Visualization,Roboflow Dataset Upload,Instance Segmentation Model,Single-Label Classification Model,VLM as Classifier,Stitch Images,Blur Visualization
The available connections depend on its binding kinds. Check what binding kinds
Crop Visualization in version v1 has.
Bindings
-
input
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..predictions(Union[object_detection_prediction,keypoint_detection_prediction,instance_segmentation_prediction]): Predictions.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..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
}