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:
Stitch Images,Pixelate Visualization,Path Deviation,Multi-Label Classification Model,LMM For Classification,Keypoint Detection Model,Gaze Detection,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,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,Dominant Color,Byte Tracker,Triangle Visualization,Bounding Rectangle,Absolute Static Crop,Object Detection Model,Time in Zone,Detections Stitch,Florence-2 Model,SIFT Comparison,Keypoint Detection Model,Corner Visualization,Perspective Correction,Local File Sink,Polygon Zone Visualization,VLM as Classifier,Image Slicer,Trace Visualization,Detections Consensus,Webhook Sink,OpenAI,Roboflow Custom Metadata,Twilio SMS Notification,Crop Visualization,Instance Segmentation Model,Roboflow Dataset Upload,Clip Comparison,Anthropic Claude,Image Blur,Circle Visualization,Image Convert Grayscale,Dot Visualization,Google Gemini,Segment Anything 2 Model,Identify Outliers,Time in Zone,Florence-2 Model,Detections Stabilizer,Path Deviation,OpenAI,Color Visualization,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
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]): The output of a detection model describing the bounding boxes that will be used to crop the image..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"
}