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