Image Contours¶
Class: ImageContoursDetectionBlockV1
Source: inference.core.workflows.core_steps.classical_cv.contours.v1.ImageContoursDetectionBlockV1
Finds the contours in an image. It returns the contours and number of contours. The input image should be thresholded before using this block.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/contours_detection@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
line_thickness |
int |
Line thickness for drawing contours.. | ✅ |
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 Image Contours in version v1.
- inputs:
Stitch Images,Pixelate Visualization,SIFT Comparison,Line Counter,Corner Visualization,Blur Visualization,Mask Visualization,Perspective Correction,SIFT,Line Counter,Polygon Zone Visualization,Polygon Visualization,Halo Visualization,Grid Visualization,Image Slicer,Model Comparison Visualization,Trace Visualization,Camera Focus,Image Threshold,Keypoint Visualization,Crop Visualization,Template Matching,Image Preprocessing,SIFT Comparison,Image Blur,Relative Static Crop,Circle Visualization,Image Convert Grayscale,Dot Visualization,Background Color Visualization,Bounding Box Visualization,Ellipse Visualization,Image Contours,Label Visualization,Classification Label Visualization,Line Counter Visualization,Stability AI Inpainting,Reference Path Visualization,Dynamic Crop,Color Visualization,Triangle Visualization,Pixel Color Count,Absolute Static Crop,Distance Measurement - 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,Grid Visualization,Google Vision OCR,Model Comparison Visualization,Email Notification,Camera Focus,CogVLM,Byte Tracker,Image Threshold,Keypoint Visualization,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,Image Contours,Label Visualization,Line Counter Visualization,Classification Label Visualization,Ellipse Visualization,Byte Tracker,LMM,Reference Path Visualization,Stability AI Inpainting,VLM as Detector,Dynamic Crop,Dominant Color,Byte Tracker,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,Webhook Sink,OpenAI,Detections Consensus,Twilio SMS Notification,Crop Visualization,Instance Segmentation Model,Buffer,Roboflow Dataset Upload,Clip Comparison,VLM as Classifier,Anthropic Claude,SIFT Comparison,Image Blur,Dot Visualization,Image Convert Grayscale,Circle Visualization,Google Gemini,QR Code Detection,Dynamic Zone,Segment Anything 2 Model,Single-Label Classification Model,Identify Outliers,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
Image Contours in version v1 has.
Bindings
-
input
-
output
image(image): Image in workflows.contours(contours): List of numpy arrays where each array represents contour points.hierarchy(numpy_array): Numpy array.number_contours(integer): Integer value.
Example JSON definition of step Image Contours in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/contours_detection@v1",
"image": "$inputs.image",
"line_thickness": 3
}