SIFT Comparison

v2

Class: SIFTComparisonBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.classical_cv.sift_comparison.v2.SIFTComparisonBlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Compare SIFT descriptors from multiple images using FLANN-based matcher.

This block is useful for determining if multiple images match based on their SIFT descriptors.

Type identifier

Use the following identifier in step "type" field: roboflow_core/sift_comparison@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
good_matches_threshold	int	Threshold for the number of good matches to consider the images as matching.	✅
ratio_threshold	float	Ratio threshold for the ratio test, which is used to filter out poor matches by comparing the distance of the closest match to the distance of the second closest match. A lower ratio indicates stricter filtering..	✅
matcher	str	Matcher to use for comparing the SIFT descriptors.	✅
visualize	bool	Whether to visualize the keypoints and matches between the two images.	✅

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 Comparison in version v2.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds SIFT Comparison in version v2 has.

Bindings

• input

  • input_1 (Union[image, numpy_array]): Reference to Image or SIFT descriptors from the first image to compare.
  • input_2 (Union[image, numpy_array]): Reference to Image or SIFT descriptors from the second image to compare.
  • good_matches_threshold (integer): Threshold for the number of good matches to consider the images as matching.
  • ratio_threshold (float_zero_to_one): Ratio threshold for the ratio test, which is used to filter out poor matches by comparing the distance of the closest match to the distance of the second closest match. A lower ratio indicates stricter filtering..
  • matcher (string): Matcher to use for comparing the SIFT descriptors.
  • visualize (boolean): Whether to visualize the keypoints and matches between the two images.

• output

  • images_match (boolean): Boolean flag.
  • good_matches_count (integer): Integer value.
  • keypoints_1 (image_keypoints): Image keypoints detected by classical Computer Vision method.
  • descriptors_1 (numpy_array): Numpy array.
  • keypoints_2 (image_keypoints): Image keypoints detected by classical Computer Vision method.
  • descriptors_2 (numpy_array): Numpy array.
  • visualization_1 (image): Image in workflows.
  • visualization_2 (image): Image in workflows.
  • visualization_matches (image): Image in workflows.

Example JSON definition of step SIFT Comparison in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/sift_comparison@v2",
    "input_1": "$inputs.image1",
    "input_2": "$inputs.image2",
    "good_matches_threshold": 50,
    "ratio_threshold": 0.7,
    "matcher": "FlannBasedMatcher",
    "visualize": true
}

v1

Class: SIFTComparisonBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.classical_cv.sift_comparison.v1.SIFTComparisonBlockV1

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Compare SIFT descriptors from multiple images using FLANN-based matcher.

This block is useful for determining if multiple images match based on their SIFT descriptors.

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
good_matches_threshold	int	Threshold for the number of good matches to consider the images as matching.	✅
ratio_threshold	float	Ratio threshold for the ratio test, which is used to filter out poor matches by comparing the distance of the closest match to the distance of the second closest match. A lower ratio indicates stricter filtering..	✅

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 Comparison in version v1.

• inputs: SIFT, Line Counter, Line Counter, Image Contours, SIFT Comparison, Perspective Correction, Template Matching, SIFT Comparison, Depth Estimation, Distance Measurement, Pixel Color Count
• outputs: Anthropic Claude, Crop Visualization, Stitch OCR Detections, Blur Visualization, PTZ Tracking (ONVIF).md), Line Counter Visualization, Color Visualization, Image Contours, Mask Visualization, Circle Visualization, Absolute Static Crop, Object Detection Model, Dynamic Zone, Multi-Label Classification Model, Keypoint Detection Model, Byte Tracker, Detections Consensus, Stitch Images, Trace Visualization, Image Preprocessing, Roboflow Custom Metadata, Object Detection Model, Polygon Zone Visualization, QR Code Generator, Halo Visualization, Perspective Correction, Stability AI Inpainting, Template Matching, Webhook Sink, Label Visualization, Pixel Color Count, Segment Anything 2 Model, Triangle Visualization, Keypoint Detection Model, Background Color Visualization, Slack Notification, Detections Stabilizer, Corner Visualization, Byte Tracker, Multi-Label Classification Model, Icon Visualization, SIFT Comparison, Pixelate Visualization, Image Blur, Gaze Detection, Model Comparison Visualization, Instance Segmentation Model, Time in Zone, Image Threshold, Reference Path Visualization, Image Slicer, Roboflow Dataset Upload, Byte Tracker, Identify Outliers, Roboflow Dataset Upload, Detection Offset, Single-Label Classification Model, Identify Changes, Classification Label Visualization, Polygon Visualization, Keypoint Visualization, Stability AI Outpainting, Dot Visualization, Time in Zone, Email Notification, Grid Visualization, Single-Label Classification Model, Bounding Box Visualization, Detections Classes Replacement, Ellipse Visualization, Model Monitoring Inference Aggregator, Twilio SMS Notification, Instance Segmentation Model, Image Slicer, SIFT Comparison, Dominant Color

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds SIFT Comparison in version v1 has.

Bindings

• input

  • descriptor_1 (numpy_array): Reference to SIFT descriptors from the first image to compare.
  • descriptor_2 (numpy_array): Reference to SIFT descriptors from the second image to compare.
  • good_matches_threshold (integer): Threshold for the number of good matches to consider the images as matching.
  • ratio_threshold (integer): Ratio threshold for the ratio test, which is used to filter out poor matches by comparing the distance of the closest match to the distance of the second closest match. A lower ratio indicates stricter filtering..

• output

  • good_matches_count (integer): Integer value.
  • images_match (boolean): Boolean flag.

Example JSON definition of step SIFT Comparison in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/sift_comparison@v1",
    "descriptor_1": "$steps.sift.descriptors",
    "descriptor_2": "$steps.sift.descriptors",
    "good_matches_threshold": 50,
    "ratio_threshold": 0.7
}