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War on Whitespace, master edition: trailing spaces.

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Roman Donchenko
2013-08-27 13:57:24 +04:00
parent 2c4bbb313c
commit 9b92545ce6
40 changed files with 263 additions and 263 deletions
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62
modules/objdetect/include/opencv2/objdetect/erfilter.hpp
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@@ -52,12 +52,12 @@ namespace cv
{
/*!
Extremal Region Stat structure
Extremal Region Stat structure
The ERStat structure represents a class-specific Extremal Region (ER).
An ER is a 4-connected set of pixels with all its grey-level values smaller than the values
in its outer boundary. A class-specific ER is selected (using a classifier) from all the ER's
An ER is a 4-connected set of pixels with all its grey-level values smaller than the values
in its outer boundary. A class-specific ER is selected (using a classifier) from all the ER's
in the component tree of the image.
*/
struct CV_EXPORTS ERStat
@@ -69,17 +69,17 @@ public:
~ERStat(){};
//! seed point and the threshold (max grey-level value)
int pixel;
int level;
int pixel;
int level;
//! incrementally computable features
int area;
int area;
int perimeter;
int euler; //!< euler number
Rect rect;
double raw_moments[2]; //!< order 1 raw moments to derive the centroid
double central_moments[3]; //!< order 2 central moments to construct the covariance matrix
std::deque<int> *crossings;//!< horizontal crossings
std::deque<int> *crossings;//!< horizontal crossings
float med_crossings; //!< median of the crossings at three different height levels
//! 2nd stage features
@@ -88,21 +88,21 @@ public:
float num_inflexion_points;
// TODO Other features can be added (average color, standard deviation, and such)
// TODO shall we include the pixel list whenever available (i.e. after 2nd stage) ?
std::vector<int> *pixels;
std::vector<int> *pixels;
//! probability that the ER belongs to the class we are looking for
double probability;
//! pointers preserving the tree structure of the component tree
ERStat* parent;
ERStat* child;
ERStat* parent;
ERStat* child;
ERStat* next;
ERStat* prev;
//! wenever the regions is a local maxima of the probability
//! wenever the regions is a local maxima of the probability
bool local_maxima;
ERStat* max_probability_ancestor;
ERStat* min_probability_ancestor;
@@ -124,11 +124,11 @@ public:
public:
virtual ~Callback(){};
//! The classifier must return probability measure for the region.
virtual double eval(const ERStat& stat) = 0; //const = 0; //TODO why cannot use const = 0 here?
virtual double eval(const ERStat& stat) = 0; //const = 0; //TODO why cannot use const = 0 here?
};
/*!
the key method. Takes image on input and returns the selected regions in a vector of ERStat
/*!
the key method. Takes image on input and returns the selected regions in a vector of ERStat
only distinctive ERs which correspond to characters are selected by a sequential classifier
\param image is the input image
\param regions is output for the first stage, input/output for the second one.
@@ -151,15 +151,15 @@ public:
/*!
Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm
Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
The component tree of the image is extracted by a threshold increased step by step
from 0 to 255, incrementally computable descriptors (aspect_ratio, compactness,
number of holes, and number of horizontal crossings) are computed for each ER
and used as features for a classifier which estimates the class-conditional
probability P(er|character). The value of P(er|character) is tracked using the inclusion
relation of ER across all thresholds and only the ERs which correspond to local maximum
from 0 to 255, incrementally computable descriptors (aspect_ratio, compactness,
number of holes, and number of horizontal crossings) are computed for each ER
and used as features for a classifier which estimates the class-conditional
probability P(er|character). The value of P(er|character) is tracked using the inclusion
relation of ER across all thresholds and only the ERs which correspond to local maximum
of the probability P(er|character) are selected (if the local maximum of the
probability is above a global limit pmin and the difference between local maximum and
probability is above a global limit pmin and the difference between local maximum and
local minimum is greater than minProbabilityDiff).
\param cb Callback with the classifier.
@@ -168,29 +168,29 @@ public:
\param minArea The minimum area (% of image size) allowed for retreived ER's
\param minArea The maximum area (% of image size) allowed for retreived ER's
\param minProbability The minimum probability P(er|character) allowed for retreived ER's
\param nonMaxSuppression Whenever non-maximum suppression is done over the branch probabilities
\param nonMaxSuppression Whenever non-maximum suppression is done over the branch probabilities
\param minProbability The minimum probability difference between local maxima and local minima ERs
*/
CV_EXPORTS Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb = NULL,
int thresholdDelta = 1, float minArea = 0.000025,
float maxArea = 0.13, float minProbability = 0.2,
bool nonMaxSuppression = true,
CV_EXPORTS Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb = NULL,
int thresholdDelta = 1, float minArea = 0.000025,
float maxArea = 0.13, float minProbability = 0.2,
bool nonMaxSuppression = true,
float minProbabilityDiff = 0.1);
/*!
Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm
Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
In the second stage, the ERs that passed the first stage are classified into character
In the second stage, the ERs that passed the first stage are classified into character
and non-character classes using more informative but also more computationally expensive
features. The classifier uses all the features calculated in the first stage and the following
features. The classifier uses all the features calculated in the first stage and the following
additional features: hole area ratio, convex hull ratio, and number of outer inflexion points.
\param cb Callback with the classifier
if omitted tries to load a default classifier from file trained_classifierNM2.xml
\param minProbability The minimum probability P(er|character) allowed for retreived ER's
*/
CV_EXPORTS Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb = NULL,
CV_EXPORTS Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb = NULL,
float minProbability = 0.85);
}