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

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

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
}