Module opencv::features2d
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2D Features Framework
Feature Detection and Description
Descriptor Matchers
Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. This section is devoted to matching descriptors that are represented as vectors in a multidimensional space. All objects that implement vector descriptor matchers inherit the DescriptorMatcher interface.
Drawing Function of Keypoints and Matches
Object Categorization
This section describes approaches based on local 2D features and used to categorize objects.
Hardware Acceleration Layer
Modules
Structs
- Brute-force descriptor matcher.
- Class to compute an image descriptor using the bag of visual words.
- kmeans -based class to train visual vocabulary using the bag of visual words approach. :
- Flann-based descriptor matcher.
- A class filters a vector of keypoints.
Enums
Constants
- Upright descriptors, not invariant to rotation
- Upright descriptors, not invariant to rotation
- Output image matrix will be created (Mat::create), i.e. existing memory of output image may be reused. Two source image, matches and single keypoints will be drawn. For each keypoint only the center point will be drawn (without the circle around keypoint with keypoint size and orientation).
- Output image matrix will not be created (Mat::create). Matches will be drawn on existing content of output image.
- For each keypoint the circle around keypoint with keypoint size and orientation will be drawn.
- Single keypoints will not be drawn.
Traits
- Class implementing the AKAZE keypoint detector and descriptor extractor, described in ANB13.
- Constant methods for crate::features2d::AKAZE
- Class for implementing the wrapper which makes detectors and extractors to be affine invariant, described as ASIFT in YM11 .
- Constant methods for crate::features2d::AffineFeature
- Wrapping class for feature detection using the AGAST method. :
- Constant methods for crate::features2d::AgastFeatureDetector
- Mutable methods for crate::features2d::BFMatcher
- Constant methods for crate::features2d::BFMatcher
- Mutable methods for crate::features2d::BOWImgDescriptorExtractor
- Constant methods for crate::features2d::BOWImgDescriptorExtractor
- Mutable methods for crate::features2d::BOWKMeansTrainer
- Constant methods for crate::features2d::BOWKMeansTrainer
- Abstract base class for training the bag of visual words vocabulary from a set of descriptors.
- Constant methods for crate::features2d::BOWTrainer
- Class implementing the BRISK keypoint detector and descriptor extractor, described in LCS11 .
- Constant methods for crate::features2d::BRISK
- Abstract base class for matching keypoint descriptors.
- Constant methods for crate::features2d::DescriptorMatcher
- Wrapping class for feature detection using the FAST method. :
- Constant methods for crate::features2d::FastFeatureDetector
- Mutable methods for crate::features2d::Feature2D
- Constant methods for crate::features2d::Feature2D
- Mutable methods for crate::features2d::FlannBasedMatcher
- Constant methods for crate::features2d::FlannBasedMatcher
- Wrapping class for feature detection using the goodFeaturesToTrack function. :
- Constant methods for crate::features2d::GFTTDetector
- Class implementing the KAZE keypoint detector and descriptor extractor, described in ABD12 .
- Constant methods for crate::features2d::KAZE
- Mutable methods for crate::features2d::KeyPointsFilter
- Constant methods for crate::features2d::KeyPointsFilter
- Maximally stable extremal region extractor
- Constant methods for crate::features2d::MSER
- Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor
- Constant methods for crate::features2d::ORB
- Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe Lowe04 .
- Constant methods for crate::features2d::SIFT
- Class for extracting blobs from an image. :
- Constant methods for crate::features2d::SimpleBlobDetector
Functions
- Detects corners using the AGAST algorithm
- Detects corners using the AGAST algorithm
- Draws keypoints.
- Draws the found matches of keypoints from two images.
- Draws the found matches of keypoints from two images.
- C++ default parameters
Functions to evaluate the feature detectors and [generic] descriptor extractors * **- Detects corners using the FAST algorithm
- Detects corners using the FAST algorithm
Type Definitions
- Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. This section is devoted to computing descriptors represented as vectors in a multidimensional space. All objects that implement the vector descriptor extractors inherit the DescriptorExtractor interface.
- Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. All objects that implement keypoint detectors inherit the FeatureDetector interface.