SIFT¶
Class: SIFTBlockV1
Source: inference.core.workflows.core_steps.classical_cv.sift.v1.SIFTBlockV1
The Scale-Invariant Feature Transform (SIFT) algorithm is a popular method in computer vision for detecting and describing features (interesting parts) in images. SIFT is used to find key points in an image and describe them in a way that allows for recognizing the same objects or features in different images, even if the images are taken from different angles, distances, or lighting conditions.
Read more: https://en.wikipedia.org/wiki/Scale-invariant_feature_transform
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/sift@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
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 SIFT in version v1.
- inputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Bounding Box Visualization,Line Counter Visualization,Image Preprocessing,Trace Visualization,Label Visualization,Polygon Zone Visualization,Camera Focus,Image Slicer,Image Slicer,Image Blur,Crop Visualization,Dot Visualization,Relative Static Crop,Model Comparison Visualization,Stability AI Inpainting,Pixelate Visualization,Perspective Correction,Image Convert Grayscale,Absolute Static Crop,Mask Visualization,Stability AI Image Generation,Color Visualization,Image Threshold,Dynamic Crop,Halo Visualization,Polygon Visualization,Image Contours,Camera Calibration,SIFT,Reference Path Visualization,Classification Label Visualization,Triangle Visualization,SIFT Comparison,Keypoint Visualization,Grid Visualization,Ellipse Visualization,Stitch Images,Blur Visualization - outputs:
Circle Visualization,Background Color Visualization,Corner Visualization,VLM as Detector,VLM as Classifier,LMM,Polygon Zone Visualization,Camera Focus,Image Slicer,Image Blur,Dot Visualization,Google Gemini,Roboflow Dataset Upload,Single-Label Classification Model,Stability AI Inpainting,Pixelate Visualization,OpenAI,Gaze Detection,Dominant Color,Stability AI Image Generation,Image Convert Grayscale,Absolute Static Crop,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,VLM as Classifier,CogVLM,Instance Segmentation Model,Camera Calibration,Object Detection Model,Classification Label Visualization,Single-Label Classification Model,Llama 3.2 Vision,Google Vision OCR,Roboflow Dataset Upload,Byte Tracker,Ellipse Visualization,Pixel Color Count,Bounding Box Visualization,Object Detection Model,Line Counter Visualization,Image Preprocessing,Keypoint Detection Model,Trace Visualization,Label Visualization,Clip Comparison,Image Slicer,Anthropic Claude,Crop Visualization,Detections Stitch,OCR Model,YOLO-World Model,Model Comparison Visualization,Relative Static Crop,QR Code Detection,Perspective Correction,OpenAI,Qwen2.5-VL,Mask Visualization,Time in Zone,Clip Comparison,Dynamic Crop,Template Matching,Florence-2 Model,Barcode Detection,Instance Segmentation Model,Keypoint Detection Model,Image Contours,Buffer,SIFT,SIFT Comparison,Multi-Label Classification Model,Florence-2 Model,Reference Path Visualization,CLIP Embedding Model,Triangle Visualization,SIFT Comparison,VLM as Detector,Keypoint Visualization,Multi-Label Classification Model,LMM For Classification,Segment Anything 2 Model,Detections Stabilizer,Stitch Images,Blur Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
SIFT in version v1 has.
Bindings
-
input
image(image): The input image for this step..
-
output
image(image): Image in workflows.keypoints(image_keypoints): Image keypoints detected by classical Computer Vision method.descriptors(numpy_array): Numpy array.
Example JSON definition of step SIFT in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/sift@v1",
"image": "$inputs.image"
}