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#1205 fixed more bugs/typos in parameters

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This commit is contained in:
Andrey Kamaev
2012-03-29 08:07:57 +00:00
parent 008a1c91fd
commit ec793df30f
13 changed files with 283 additions and 289 deletions
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64
modules/features2d/doc/common_interfaces_of_feature_detectors.rst
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@@ -15,27 +15,27 @@ KeyPoint
Data structure for salient point detectors.
.. ocv:member:: Point2f pt
coordinates of the keypoint
.. ocv:member:: float size
diameter of the meaningful keypoint neighborhood
.. ocv:member:: float angle
computed orientation of the keypoint (-1 if not applicable)
.. ocv:member:: float response
the response by which the most strong keypoints have been selected. Can be used for further sorting or subsampling
.. ocv:member:: int octave
octave (pyramid layer) from which the keypoint has been extracted
.. ocv:member:: int class_id
object id that can be used to clustered keypoints by an object they belong to
KeyPoint::KeyPoint
@@ -51,19 +51,19 @@ The keypoint constructors
.. ocv:pyfunction:: cv2.KeyPoint(x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]) -> <KeyPoint object>
:param x: x-coordinate of the keypoint
:param y: y-coordinate of the keypoint
:param _pt: x & y coordinates of the keypoint
:param _size: keypoint diameter
:param _angle: keypoint orientation
:param _response: keypoint detector response on the keypoint (that is, strength of the keypoint)
:param _octave: pyramid octave in which the keypoint has been detected
:param _class_id: object id
@@ -309,7 +309,7 @@ Wrapping class for feature detection using the
protected:
...
};
DenseFeatureDetector
--------------------
@@ -317,18 +317,18 @@ DenseFeatureDetector
Class for generation of image features which are distributed densely and regularly over the image. ::
class DenseFeatureDetector : public FeatureDetector
{
public:
DenseFeatureDetector( float initFeatureScale=1.f, int featureScaleLevels=1,
class DenseFeatureDetector : public FeatureDetector
{
public:
DenseFeatureDetector( float initFeatureScale=1.f, int featureScaleLevels=1,
float featureScaleMul=0.1f,
int initXyStep=6, int initImgBound=0,
bool varyXyStepWithScale=true,
bool varyImgBoundWithScale=false );
protected:
protected:
...
};
The detector generates several levels (in the amount of ``featureScaleLevels``) of features. Features of each level are located in the nodes of a regular grid over the image (excluding the image boundary of given size). The level parameters (a feature scale, a node size, a size of boundary) are multiplied by ``featureScaleMul`` with level index growing depending on input flags, viz.:
* Feature scale is multiplied always.
@@ -380,11 +380,11 @@ Class for extracting blobs from an image. ::
The class implements a simple algorithm for extracting blobs from an image:
#. Convert the source image to binary images by applying thresholding with several thresholds from ``minThreshold`` (inclusive) to ``maxThreshold`` (exclusive) with distance ``thresholdStep`` between neighboring thresholds.
#. Convert the source image to binary images by applying thresholding with several thresholds from ``minThreshold`` (inclusive) to ``maxThreshold`` (exclusive) with distance ``thresholdStep`` between neighboring thresholds.
#. Extract connected components from every binary image by :ocv:func:`findContours` and calculate their centers.
#. Extract connected components from every binary image by :ocv:func:`findContours` and calculate their centers.
#. Group centers from several binary images by their coordinates. Close centers form one group that corresponds to one blob, which is controlled by the ``minDistBetweenBlobs`` parameter.
#. Group centers from several binary images by their coordinates. Close centers form one group that corresponds to one blob, which is controlled by the ``minDistBetweenBlobs`` parameter.
#. From the groups, estimate final centers of blobs and their radiuses and return as locations and sizes of keypoints.
@@ -468,7 +468,7 @@ panorama series.
``DynamicAdaptedFeatureDetector`` uses another detector, such as FAST or SURF, to do the dirty work,
with the help of ``AdjusterAdapter`` .
If the detected number of features is not large enough,
``AdjusterAdapter`` adjusts the detection parameters so that the next detection
``AdjusterAdapter`` adjusts the detection parameters so that the next detection
results in a bigger or smaller number of features. This is repeated until either the number of desired features are found
or the parameters are maxed out.
@@ -500,14 +500,14 @@ The constructor
:param max_features: Maximum desired number of features.
:param max_iters: Maximum number of times to try adjusting the feature detector parameters. For :ocv:class:`FastAdjuster` , this number can be high, but with ``Star`` or ``Surf`` many iterations can be time-comsuming. At each iteration the detector is rerun.
:param max_iters: Maximum number of times to try adjusting the feature detector parameters. For :ocv:class:`FastAdjuster` , this number can be high, but with ``Star`` or ``Surf`` many iterations can be time-comsuming. At each iteration the detector is rerun.
AdjusterAdapter
---------------
.. ocv:class:: AdjusterAdapter
Class providing an interface for adjusting parameters of a feature detector. This interface is used by :ocv:class:`DynamicAdaptedFeatureDetector` . It is a wrapper for :ocv:class:`FeatureDetector` that enables adjusting parameters after feature detection. ::
class AdjusterAdapter: public FeatureDetector
{
public:
@@ -562,7 +562,7 @@ Example: ::
AdjusterAdapter::good
-------------------------
Returns false if the detector parameters cannot be adjusted any more.
Returns false if the detector parameters cannot be adjusted any more.
.. ocv:function:: bool AdjusterAdapter::good() const