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: Stability AI Inpainting, Line Counter, Template Matching, Label Visualization, Depth Estimation, Corner Visualization, Triangle Visualization, Background Color Visualization, Image Blur, Polygon Zone Visualization, Model Comparison Visualization, Line Counter Visualization, Camera Focus, Circle Visualization, Perspective Correction, Relative Static Crop, Grid Visualization, Stability AI Image Generation, Line Counter, Trace Visualization, Image Slicer, Blur Visualization, Classification Label Visualization, Image Convert Grayscale, Image Preprocessing, SIFT Comparison, Pixel Color Count, Stitch Images, Reference Path Visualization, Stability AI Outpainting, Polygon Visualization, Camera Calibration, Mask Visualization, SIFT, Bounding Box Visualization, Image Threshold, Distance Measurement, Keypoint Visualization, Ellipse Visualization, Crop Visualization, Color Visualization, Pixelate Visualization, Image Slicer, Dynamic Crop, Image Contours, Absolute Static Crop, Halo Visualization, SIFT Comparison, Dot Visualization
• outputs: Florence-2 Model, Label Visualization, Florence-2 Model, Depth Estimation, Triangle Visualization, CogVLM, Image Blur, OCR Model, Model Comparison Visualization, Line Counter Visualization, Circle Visualization, Relative Static Crop, Barcode Detection, Detections Stitch, Trace Visualization, Byte Tracker, Multi-Label Classification Model, Object Detection Model, Clip Comparison, Detections Consensus, Detection Offset, Gaze Detection, Stitch Images, Dominant Color, Reference Path Visualization, Llama 3.2 Vision, Polygon Visualization, Time in Zone, Segment Anything 2 Model, Roboflow Dataset Upload, Identify Outliers, Single-Label Classification Model, SIFT, Image Threshold, CLIP Embedding Model, VLM as Classifier, Keypoint Visualization, Crop Visualization, Image Slicer, Color Visualization, Ellipse Visualization, Google Gemini, Dynamic Crop, OpenAI, Instance Segmentation Model, Multi-Label Classification Model, Dot Visualization, Instance Segmentation Model, Roboflow Dataset Upload, Keypoint Detection Model, SmolVLM2, Keypoint Detection Model, Stability AI Inpainting, Identify Changes, Google Vision OCR, Single-Label Classification Model, Template Matching, Corner Visualization, Background Color Visualization, Polygon Zone Visualization, Byte Tracker, Camera Focus, Stability AI Image Generation, Perspective Correction, VLM as Detector, Grid Visualization, Image Slicer, OpenAI, Qwen2.5-VL, Clip Comparison, Blur Visualization, Dynamic Zone, Classification Label Visualization, Image Convert Grayscale, Image Preprocessing, Slack Notification, SIFT Comparison, Byte Tracker, Pixel Color Count, Detections Stabilizer, OpenAI, YOLO-World Model, Stability AI Outpainting, Perception Encoder Embedding Model, QR Code Detection, Anthropic Claude, Moondream2, Webhook Sink, Camera Calibration, Buffer, Mask Visualization, Bounding Box Visualization, Pixelate Visualization, Twilio SMS Notification, Email Notification, PTZ Tracking (ONVIF).md), Object Detection Model, Image Contours, Detections Classes Replacement, Absolute Static Crop, Stitch OCR Detections, Halo Visualization, LMM For Classification, SIFT Comparison, VLM as Detector, LMM, VLM as Classifier

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Image Contours in version v1 has.

Bindings

• input

  • image (image): The input image for this step..
  • line_thickness (integer): Line thickness for drawing contours..

• 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
}