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265 lines
6.8 KiB
ReStructuredText
265 lines
6.8 KiB
ReStructuredText
Feature detection and description
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=================================
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.. highlight:: python
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* **image** The image. Keypoints (corners) will be detected on this.
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* **keypoints** Keypoints detected on the image.
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* **threshold** Threshold on difference between intensity of center pixel and
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pixels on circle around this pixel. See description of the algorithm.
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* **nonmaxSupression** If it is true then non-maximum supression will be applied to detected corners (keypoints).
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.. index:: CvSURFPoint
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.. _CvSURFPoint:
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CvSURFPoint
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-----------
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.. class:: CvSURFPoint
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A SURF keypoint, represented as a tuple
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``((x, y), laplacian, size, dir, hessian)``
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.
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.. attribute:: x
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x-coordinate of the feature within the image
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.. attribute:: y
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y-coordinate of the feature within the image
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.. attribute:: laplacian
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-1, 0 or +1. sign of the laplacian at the point. Can be used to speedup feature comparison since features with laplacians of different signs can not match
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.. attribute:: size
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size of the feature
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.. attribute:: dir
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orientation of the feature: 0..360 degrees
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.. attribute:: hessian
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value of the hessian (can be used to approximately estimate the feature strengths; see also params.hessianThreshold)
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.. index:: ExtractSURF
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.. _ExtractSURF:
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ExtractSURF
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-----------
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.. function:: ExtractSURF(image,mask,storage,params)-> (keypoints,descriptors)
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Extracts Speeded Up Robust Features from an image.
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:param image: The input 8-bit grayscale image
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:type image: :class:`CvArr`
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:param mask: The optional input 8-bit mask. The features are only found in the areas that contain more than 50 % of non-zero mask pixels
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:type mask: :class:`CvArr`
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:param keypoints: sequence of keypoints.
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:type keypoints: :class:`CvSeq` of :class:`CvSURFPoint`
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:param descriptors: sequence of descriptors. Each SURF descriptor is a list of floats, of length 64 or 128.
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:type descriptors: :class:`CvSeq` of list of float
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:param storage: Memory storage where keypoints and descriptors will be stored
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:type storage: :class:`CvMemStorage`
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:param params: Various algorithm parameters in a tuple ``(extended, hessianThreshold, nOctaves, nOctaveLayers)`` :
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* **extended** 0 means basic descriptors (64 elements each), 1 means extended descriptors (128 elements each)
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* **hessianThreshold** only features with hessian larger than that are extracted. good default value is ~300-500 (can depend on the average local contrast and sharpness of the image). user can further filter out some features based on their hessian values and other characteristics.
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* **nOctaves** the number of octaves to be used for extraction. With each next octave the feature size is doubled (3 by default)
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* **nOctaveLayers** The number of layers within each octave (4 by default)
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:type params: :class:`CvSURFParams`
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The function cvExtractSURF finds robust features in the image, as
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described in
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Bay06
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. For each feature it returns its location, size,
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orientation and optionally the descriptor, basic or extended. The function
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can be used for object tracking and localization, image stitching etc.
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To extract strong SURF features from an image
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.. doctest::
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>>> import cv
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>>> im = cv.LoadImageM("building.jpg", cv.CV_LOAD_IMAGE_GRAYSCALE)
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>>> (keypoints, descriptors) = cv.ExtractSURF(im, None, cv.CreateMemStorage(), (0, 30000, 3, 1))
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>>> print len(keypoints), len(descriptors)
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6 6
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>>> for ((x, y), laplacian, size, dir, hessian) in keypoints:
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... print "x=%d y=%d laplacian=%d size=%d dir=%f hessian=%f" % (x, y, laplacian, size, dir, hessian)
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x=30 y=27 laplacian=-1 size=31 dir=69.778503 hessian=36979.789062
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x=296 y=197 laplacian=1 size=33 dir=111.081039 hessian=31514.349609
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x=296 y=266 laplacian=1 size=32 dir=107.092300 hessian=31477.908203
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x=254 y=284 laplacian=1 size=31 dir=279.137360 hessian=34169.800781
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x=498 y=525 laplacian=-1 size=33 dir=278.006592 hessian=31002.759766
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x=777 y=281 laplacian=1 size=70 dir=167.940964 hessian=35538.363281
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..
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.. index:: GetStarKeypoints
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.. _GetStarKeypoints:
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GetStarKeypoints
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----------------
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.. function:: GetStarKeypoints(image,storage,params)-> keypoints
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Retrieves keypoints using the StarDetector algorithm.
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:param image: The input 8-bit grayscale image
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:type image: :class:`CvArr`
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:param storage: Memory storage where the keypoints will be stored
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:type storage: :class:`CvMemStorage`
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:param params: Various algorithm parameters in a tuple ``(maxSize, responseThreshold, lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize)`` :
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* **maxSize** maximal size of the features detected. The following values of the parameter are supported: 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128
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* **responseThreshold** threshold for the approximatd laplacian, used to eliminate weak features
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* **lineThresholdProjected** another threshold for laplacian to eliminate edges
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* **lineThresholdBinarized** another threshold for the feature scale to eliminate edges
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* **suppressNonmaxSize** linear size of a pixel neighborhood for non-maxima suppression
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:type params: :class:`CvStarDetectorParams`
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The function GetStarKeypoints extracts keypoints that are local
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scale-space extremas. The scale-space is constructed by computing
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approximate values of laplacians with different sigma's at each
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pixel. Instead of using pyramids, a popular approach to save computing
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time, all of the laplacians are computed at each pixel of the original
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high-resolution image. But each approximate laplacian value is computed
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in O(1) time regardless of the sigma, thanks to the use of integral
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images. The algorithm is based on the paper
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Agrawal08
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, but instead
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of a square, hexagon or octagon it uses an 8-end star shape, hence the name,
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consisting of overlapping upright and tilted squares.
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Each keypoint is represented by a tuple
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``((x, y), size, response)``
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:
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* **x, y** Screen coordinates of the keypoint
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* **size** feature size, up to ``maxSize``
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* **response** approximated laplacian value for the keypoint
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