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

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[numpy_array, image]): Reference to Image or SIFT descriptors from the first image to compare.
  • input_2 (Union[numpy_array, image]): 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: Perspective Correction, Pixel Color Count, SIFT Comparison, Image Contours, Depth Estimation, SIFT, Distance Measurement, SIFT Comparison, Line Counter, Template Matching, Line Counter
• outputs: Keypoint Detection Model, Circle Visualization, Roboflow Dataset Upload, Gaze Detection, PTZ Tracking (ONVIF).md), Roboflow Custom Metadata, Single-Label Classification Model, Template Matching, Grid Visualization, Dynamic Zone, Instance Segmentation Model, Color Visualization, Detections Consensus, Object Detection Model, Identify Outliers, Perspective Correction, Model Monitoring Inference Aggregator, Image Slicer, Keypoint Detection Model, Model Comparison Visualization, Stitch Images, Image Contours, Webhook Sink, Pixelate Visualization, Byte Tracker, SIFT Comparison, Reference Path Visualization, Image Blur, Time in Zone, Blur Visualization, Ellipse Visualization, Trace Visualization, Corner Visualization, Polygon Zone Visualization, Detections Classes Replacement, Triangle Visualization, Stability AI Inpainting, Classification Label Visualization, Single-Label Classification Model, Bounding Box Visualization, Detections Stabilizer, Halo Visualization, Email Notification, Polygon Visualization, Absolute Static Crop, Slack Notification, Object Detection Model, Byte Tracker, Dot Visualization, Label Visualization, Stability AI Outpainting, Crop Visualization, Detection Offset, Pixel Color Count, Image Threshold, Stitch OCR Detections, Image Preprocessing, Identify Changes, SIFT Comparison, Mask Visualization, Time in Zone, Dominant Color, Twilio SMS Notification, Segment Anything 2 Model, Roboflow Dataset Upload, Byte Tracker, Line Counter Visualization, Anthropic Claude, Instance Segmentation Model, Background Color Visualization, Multi-Label Classification Model, Image Slicer, Keypoint Visualization, Multi-Label Classification Model

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
}