Stitch Images

Class: StitchImagesBlockV1

Source: inference.core.workflows.core_steps.transformations.stitch_images.v1.StitchImagesBlockV1

This block combines two related scenes both containing fair amount of details. Block is utilizing Scale Invariant Feature Transform (SIFT)

Type identifier

Use the following identifier in step "type" field: roboflow_core/stitch_images@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
max_allowed_reprojection_error	float	Advanced parameter overwriting cv.findHomography ransacReprojThreshold parameter. Maximum allowed reprojection error to treat a point pair as an inlier. Increasing value of this parameter for low details photo may yield better results..	✅
count_of_best_matches_per_query_descriptor	int	Advanced parameter overwriting cv.BFMatcher.knnMatch k parameter. Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total..	✅

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 Stitch Images in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Stitch Images in version v1 has.

Bindings

• input

  • image1 (image): First input image for this step..
  • image2 (image): Second input image for this step..
  • max_allowed_reprojection_error (float_zero_to_one): Advanced parameter overwriting cv.findHomography ransacReprojThreshold parameter. Maximum allowed reprojection error to treat a point pair as an inlier. Increasing value of this parameter for low details photo may yield better results..
  • count_of_best_matches_per_query_descriptor (integer): Advanced parameter overwriting cv.BFMatcher.knnMatch k parameter. Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total..

• output

  • stitched_image (image): Image in workflows.

Example JSON definition of step Stitch Images in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/stitch_images@v1",
    "image1": "$inputs.image1",
    "image2": "$inputs.image2",
    "max_allowed_reprojection_error": 3,
    "count_of_best_matches_per_query_descriptor": 2
}