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

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
}