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@v1
to 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:
Polygon Zone Visualization
,Camera Calibration
,Dot Visualization
,Depth Estimation
,Image Slicer
,QR Code Generator
,Morphological Transformation
,Trace Visualization
,Blur Visualization
,Perspective Correction
,Corner Visualization
,Contrast Equalization
,Crop Visualization
,Stability AI Image Generation
,Grid Visualization
,Image Threshold
,Halo Visualization
,SIFT Comparison
,Model Comparison Visualization
,Pixelate Visualization
,Line Counter Visualization
,Stability AI Outpainting
,Relative Static Crop
,Image Contours
,Polygon Visualization
,Camera Focus
,Triangle Visualization
,SIFT
,Image Slicer
,Image Convert Grayscale
,Classification Label Visualization
,Keypoint Visualization
,Circle Visualization
,Image Blur
,Bounding Box Visualization
,Label Visualization
,Reference Path Visualization
,Absolute Static Crop
,Stability AI Inpainting
,Icon Visualization
,Ellipse Visualization
,Dynamic Crop
,Color Visualization
,Mask Visualization
,Image Preprocessing
,Stitch Images
,Background Color Visualization
- outputs:
VLM as Classifier
,LMM For Classification
,Polygon Zone Visualization
,Dot Visualization
,Morphological Transformation
,Perspective Correction
,Blur Visualization
,Clip Comparison
,Corner Visualization
,LMM
,Pixel Color Count
,Florence-2 Model
,Image Threshold
,Florence-2 Model
,Halo Visualization
,OpenAI
,Keypoint Detection Model
,Multi-Label Classification Model
,CogVLM
,EasyOCR
,Byte Tracker
,Line Counter Visualization
,Perception Encoder Embedding Model
,VLM as Detector
,Stability AI Outpainting
,VLM as Classifier
,Keypoint Detection Model
,Google Vision OCR
,CLIP Embedding Model
,Camera Focus
,Roboflow Dataset Upload
,SIFT
,Instance Segmentation Model
,Clip Comparison
,Image Slicer
,Image Convert Grayscale
,Keypoint Visualization
,Template Matching
,OCR Model
,Llama 3.2 Vision
,Bounding Box Visualization
,Detections Stabilizer
,Instance Segmentation Model
,Reference Path Visualization
,Dynamic Crop
,Roboflow Dataset Upload
,Mask Visualization
,Image Preprocessing
,Qwen2.5-VL
,Background Color Visualization
,OpenAI
,Camera Calibration
,Depth Estimation
,Image Slicer
,Detections Stitch
,SIFT Comparison
,Dominant Color
,Trace Visualization
,Object Detection Model
,Contrast Equalization
,Buffer
,Crop Visualization
,Stability AI Image Generation
,Stitch Images
,SmolVLM2
,SIFT Comparison
,Object Detection Model
,Model Comparison Visualization
,Pixelate Visualization
,Gaze Detection
,Anthropic Claude
,Time in Zone
,QR Code Detection
,Relative Static Crop
,Polygon Visualization
,Barcode Detection
,Image Contours
,OpenAI
,Triangle Visualization
,YOLO-World Model
,Single-Label Classification Model
,Classification Label Visualization
,Circle Visualization
,Image Blur
,Label Visualization
,VLM as Detector
,Google Gemini
,Multi-Label Classification Model
,Stability AI Inpainting
,Absolute Static Crop
,Icon Visualization
,Ellipse Visualization
,Color Visualization
,Single-Label Classification Model
,Moondream2
,Segment Anything 2 Model
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"
}