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

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
}