Normalize line endings and whitespace
This commit is contained in:
OpenCV Buildbot
committed by
Andrey Kamaev
Andrey Kamaev
parent
69020da607
commit
04384a71e4
@@ -200,12 +200,12 @@ typedef struct CvLSVMFilterObject{
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// score_threshold - confidence level threshold
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typedef struct CvLatentSvmDetector
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{
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int num_filters;
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int num_components;
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int* num_part_filters;
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CvLSVMFilterObject** filters;
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float* b;
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float score_threshold;
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int num_filters;
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int num_components;
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int* num_part_filters;
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CvLSVMFilterObject** filters;
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float* b;
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float score_threshold;
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}
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CvLatentSvmDetector;
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@@ -215,8 +215,8 @@ CvLatentSvmDetector;
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// score - confidence level
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typedef struct CvObjectDetection
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{
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CvRect rect;
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float score;
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CvRect rect;
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float score;
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} CvObjectDetection;
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//////////////// Object Detection using Latent SVM //////////////
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@@ -229,7 +229,7 @@ typedef struct CvObjectDetection
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// CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename);
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// INPUT
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// filename - path to the file containing the parameters of
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- trained Latent SVM detector
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- trained Latent SVM detector
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// OUTPUT
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// trained Latent SVM detector in internal representation
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*/
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@@ -267,9 +267,9 @@ CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
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// sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures)
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*/
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CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
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CvLatentSvmDetector* detector,
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CvMemStorage* storage,
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float overlap_threshold CV_DEFAULT(0.5f),
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CvLatentSvmDetector* detector,
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CvMemStorage* storage,
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float overlap_threshold CV_DEFAULT(0.5f),
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int numThreads CV_DEFAULT(-1));
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#ifdef __cplusplus
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@@ -333,7 +333,7 @@ CV_EXPORTS void groupRectangles( vector<Rect>& rectList, int groupThreshold, dou
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CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels,
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vector<double>& levelWeights, int groupThreshold, double eps=0.2);
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CV_EXPORTS void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights, vector<double>& foundScales,
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double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
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double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
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class CV_EXPORTS FeatureEvaluator
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@@ -359,10 +359,10 @@ template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj();
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enum
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{
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CASCADE_DO_CANNY_PRUNING=1,
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CASCADE_SCALE_IMAGE=2,
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CASCADE_FIND_BIGGEST_OBJECT=4,
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CASCADE_DO_ROUGH_SEARCH=8
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CASCADE_DO_CANNY_PRUNING=1,
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CASCADE_SCALE_IMAGE=2,
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CASCADE_FIND_BIGGEST_OBJECT=4,
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CASCADE_DO_ROUGH_SEARCH=8
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};
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class CV_EXPORTS_W CascadeClassifier
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@@ -509,7 +509,7 @@ public:
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enum { DEFAULT_NLEVELS=64 };
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CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
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cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
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cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
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histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
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nlevels(HOGDescriptor::DEFAULT_NLEVELS)
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{}
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@@ -554,33 +554,33 @@ public:
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CV_OUT vector<float>& descriptors,
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Size winStride=Size(), Size padding=Size(),
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const vector<Point>& locations=vector<Point>()) const;
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//with found weights output
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//with found weights output
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CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
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CV_OUT vector<double>& weights,
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CV_OUT vector<double>& weights,
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double hitThreshold=0, Size winStride=Size(),
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Size padding=Size(),
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Size padding=Size(),
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const vector<Point>& searchLocations=vector<Point>()) const;
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//without found weights output
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//without found weights output
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virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
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double hitThreshold=0, Size winStride=Size(),
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Size padding=Size(),
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const vector<Point>& searchLocations=vector<Point>()) const;
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//with result weights output
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//with result weights output
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CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
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CV_OUT vector<double>& foundWeights, double hitThreshold=0,
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Size winStride=Size(), Size padding=Size(), double scale=1.05,
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double finalThreshold=2.0,bool useMeanshiftGrouping = false) const;
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//without found weights output
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virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
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double hitThreshold=0, Size winStride=Size(),
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CV_OUT vector<double>& foundWeights, double hitThreshold=0,
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Size winStride=Size(), Size padding=Size(), double scale=1.05,
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double finalThreshold=2.0,bool useMeanshiftGrouping = false) const;
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//without found weights output
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virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
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double hitThreshold=0, Size winStride=Size(),
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Size padding=Size(), double scale=1.05,
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double finalThreshold=2.0, bool useMeanshiftGrouping = false) const;
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double finalThreshold=2.0, bool useMeanshiftGrouping = false) const;
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CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
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Size paddingTL=Size(), Size paddingBR=Size()) const;
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CV_WRAP static vector<float> getDefaultPeopleDetector();
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CV_WRAP static vector<float> getDaimlerPeopleDetector();
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CV_WRAP static vector<float> getDaimlerPeopleDetector();
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CV_PROP Size winSize;
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CV_PROP Size blockSize;
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@@ -764,7 +764,7 @@ public:
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* in quantized image and cannot be extracted as features.
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*/
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Ptr<QuantizedPyramid> process(const Mat& src,
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const Mat& mask = Mat()) const
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const Mat& mask = Mat()) const
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{
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return processImpl(src, mask);
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}
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@@ -791,7 +791,7 @@ public:
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protected:
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// Indirection is because process() has a default parameter.
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virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
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const Mat& mask) const =0;
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const Mat& mask) const =0;
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};
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/**
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@@ -826,7 +826,7 @@ public:
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protected:
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virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
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const Mat& mask) const;
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const Mat& mask) const;
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};
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/**
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@@ -865,7 +865,7 @@ public:
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protected:
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virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
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const Mat& mask) const;
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const Mat& mask) const;
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};
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/**
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@@ -963,7 +963,7 @@ public:
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* \return Template ID, or -1 if failed to extract a valid template.
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*/
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int addTemplate(const std::vector<Mat>& sources, const std::string& class_id,
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const Mat& object_mask, Rect* bounding_box = NULL);
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const Mat& object_mask, Rect* bounding_box = NULL);
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/**
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* \brief Add a new object template computed by external means.
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