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

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

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
}