Dynamic Crop¶
Class: DynamicCropBlockV1
Source: inference.core.workflows.core_steps.transformations.dynamic_crop.v1.DynamicCropBlockV1
Create dynamic crops from an image based on detections from detections-based model.
This is useful when placed after an ObjectDetection block as part of a multi-stage workflow. For example, you could use an ObjectDetection block to detect objects, then the DynamicCropBlock block to crop objects, then an OCR block to run character recognition on each of the individual cropped regions.
In addition, for instance segmentation predictions (which provide segmentation mask for each
bounding box) it is possible to remove background in the crops, outside of detected instances.
To enable that functionality, set mask_opacity to positive value and optionally tune
background_color.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/dynamic_crop@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
mask_opacity |
float |
For instance segmentation, mask_opacity can be used to control background removal. Opacity 1.0 removes the background, while 0.0 leaves the crop unchanged.. | ✅ |
background_color |
Union[Tuple[int, int, int], str] |
For background removal based on segmentation mask, new background color can be selected. Can be a hex string (like '#431112') RGB string (like '(128, 32, 64)') or a RGB tuple (like (18, 17, 67)).. | ✅ |
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 Dynamic Crop in version v1.
- inputs:
Segment Anything 2 Model,Image Slicer,Stability AI Inpainting,Clip Comparison,Perspective Correction,Object Detection Model,Roboflow Custom Metadata,Object Detection Model,SIFT Comparison,Detection Offset,Grid Visualization,Ellipse Visualization,SIFT,VLM as Detector,CogVLM,Image Contours,OpenAI,Absolute Static Crop,Camera Focus,Trace Visualization,Multi-Label Classification Model,VLM as Detector,Dot Visualization,Google Vision OCR,Identify Changes,Polygon Zone Visualization,Gaze Detection,Identify Outliers,Roboflow Dataset Upload,Classification Label Visualization,Corner Visualization,Byte Tracker,Llama 3.2 Vision,Dynamic Crop,Reference Path Visualization,Label Visualization,Detections Stabilizer,Mask Visualization,Triangle Visualization,Line Counter Visualization,Template Matching,Dynamic Zone,Detections Transformation,Dominant Color,Time in Zone,Model Monitoring Inference Aggregator,Blur Visualization,Line Counter,Anthropic Claude,Instance Segmentation Model,Webhook Sink,Time in Zone,Instance Segmentation Model,Slack Notification,Detections Filter,Stitch OCR Detections,Pixelate Visualization,Path Deviation,OpenAI,Relative Static Crop,Detections Consensus,Twilio SMS Notification,VLM as Classifier,Keypoint Detection Model,Roboflow Dataset Upload,Google Gemini,Model Comparison Visualization,Halo Visualization,Crop Visualization,Byte Tracker,Image Blur,Circle Visualization,Velocity,Keypoint Detection Model,Image Preprocessing,Background Color Visualization,Bounding Rectangle,Florence-2 Model,Bounding Box Visualization,Byte Tracker,Florence-2 Model,Image Slicer,Local File Sink,LMM For Classification,Stitch Images,Stability AI Image Generation,Image Threshold,Detections Stitch,OCR Model,LMM,Keypoint Visualization,Email Notification,Color Visualization,Path Deviation,Single-Label Classification Model,YOLO-World Model,CSV Formatter,Image Convert Grayscale,Detections Classes Replacement,Polygon Visualization - outputs:
Segment Anything 2 Model,Image Slicer,Stability AI Inpainting,Clip Comparison,Perspective Correction,Object Detection Model,Object Detection Model,SIFT Comparison,CogVLM,Ellipse Visualization,SIFT,VLM as Detector,Image Contours,Multi-Label Classification Model,OpenAI,Absolute Static Crop,Camera Focus,Trace Visualization,CLIP Embedding Model,Multi-Label Classification Model,VLM as Detector,Dot Visualization,Google Vision OCR,Clip Comparison,Polygon Zone Visualization,Gaze Detection,Roboflow Dataset Upload,VLM as Classifier,Classification Label Visualization,Corner Visualization,Llama 3.2 Vision,Dynamic Crop,Reference Path Visualization,Detections Stabilizer,Label Visualization,Mask Visualization,Triangle Visualization,Template Matching,Line Counter Visualization,Dominant Color,Barcode Detection,Blur Visualization,Anthropic Claude,Instance Segmentation Model,Time in Zone,Instance Segmentation Model,Pixelate Visualization,OpenAI,Relative Static Crop,VLM as Classifier,Keypoint Detection Model,Roboflow Dataset Upload,Google Gemini,Model Comparison Visualization,Halo Visualization,Crop Visualization,Qwen2.5-VL,Image Blur,Circle Visualization,Buffer,Keypoint Detection Model,Image Preprocessing,Background Color Visualization,QR Code Detection,Pixel Color Count,Florence-2 Model,Single-Label Classification Model,Bounding Box Visualization,Byte Tracker,Florence-2 Model,Image Slicer,LMM For Classification,Stitch Images,Stability AI Image Generation,Image Threshold,Detections Stitch,OCR Model,LMM,Keypoint Visualization,Color Visualization,Single-Label Classification Model,YOLO-World Model,Image Convert Grayscale,Polygon Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Dynamic Crop in version v1 has.
Bindings
-
input
images(image): The input image for this step..predictions(Union[instance_segmentation_prediction,keypoint_detection_prediction,object_detection_prediction]): Detection model output containing bounding boxes for cropping..mask_opacity(float_zero_to_one): For instance segmentation, mask_opacity can be used to control background removal. Opacity 1.0 removes the background, while 0.0 leaves the crop unchanged..background_color(Union[rgb_color,string]): For background removal based on segmentation mask, new background color can be selected. Can be a hex string (like '#431112') RGB string (like '(128, 32, 64)') or a RGB tuple (like (18, 17, 67))..
-
output
crops(image): Image in workflows.
Example JSON definition of step Dynamic Crop in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/dynamic_crop@v1",
"images": "$inputs.image",
"predictions": "$steps.my_object_detection_model.predictions",
"mask_opacity": "<block_does_not_provide_example>",
"background_color": "#431112"
}