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

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
}