C# 클래스 Emgu.CV.HaarCascade

상속: Emgu.Util.UnmanagedObject

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공개 메소드들

메소드	설명
Detect ( Byte>.Image image ) : Emgu.CV.Structure.MCvAvgComp[]	Detect HaarCascade object in the given image, using predefined parameters
Detect ( Byte>.Image image, double scaleFactor, int minNeighbors, CvEnum flag, Size minSize, Size maxSize ) : Emgu.CV.Structure.MCvAvgComp[]	Finds rectangular regions in the given image that are likely to contain objects the cascade has been trained for and returns those regions as a sequence of rectangles. The function scans the image several times at different scales (see cvSetImagesForHaarClassifierCascade). Each time it considers overlapping regions in the image and applies the classifiers to the regions using cvRunHaarClassifierCascade. It may also apply some heuristics to reduce number of analyzed regions, such as Canny prunning. After it has proceeded and collected the candidate rectangles (regions that passed the classifier cascade), it groups them and returns a sequence of average rectangles for each large enough group. The default parameters (scale_factor=1.1, min_neighbors=3, flags=0) are tuned for accurate yet slow object detection. For a faster operation on real video images the settings are: scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING, min_size=<minimum possible face size> (for example, ~1/4 to 1/16 of the image area in case of video conferencing).
HaarCascade ( String fileName ) : System	Create a HaarCascade object from the specific file

보호된 메소드들

메소드	설명
DisposeObject ( ) : void	Release the HaarCascade Object and all the memory associate with it

메소드 상세

Detect() 공개 메소드

Detect HaarCascade object in the given image, using predefined parameters

public Detect ( Byte>.Image image ) : Emgu.CV.Structure.MCvAvgComp[]
image	Byte>.Image	The image where the objects are to be detected from
리턴	Emgu.CV.Structure.MCvAvgComp[]

Detect() 공개 메소드

Finds rectangular regions in the given image that are likely to contain objects the cascade has been trained for and returns those regions as a sequence of rectangles. The function scans the image several times at different scales (see cvSetImagesForHaarClassifierCascade). Each time it considers overlapping regions in the image and applies the classifiers to the regions using cvRunHaarClassifierCascade. It may also apply some heuristics to reduce number of analyzed regions, such as Canny prunning. After it has proceeded and collected the candidate rectangles (regions that passed the classifier cascade), it groups them and returns a sequence of average rectangles for each large enough group. The default parameters (scale_factor=1.1, min_neighbors=3, flags=0) are tuned for accurate yet slow object detection. For a faster operation on real video images the settings are: scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING, min_size=<minimum possible face size> (for example, ~1/4 to 1/16 of the image area in case of video conferencing).

public Detect ( Byte>.Image image, double scaleFactor, int minNeighbors, CvEnum flag, Size minSize, Size maxSize ) : Emgu.CV.Structure.MCvAvgComp[]
image	Byte>.Image	The image where the objects are to be detected from
scaleFactor	double	The factor by which the search window is scaled between the subsequent scans, for example, 1.1 means increasing window by 10%
minNeighbors	int	Minimum number (minus 1) of neighbor rectangles that makes up an object. All the groups of a smaller number of rectangles than min_neighbors-1 are rejected. If min_neighbors is 0, the function does not any grouping at all and returns all the detected candidate rectangles, which may be useful if the user wants to apply a customized grouping procedure
flag	CvEnum	Mode of operation. Currently the only flag that may be specified is CV_HAAR_DO_CANNY_PRUNING. If it is set, the function uses Canny edge detector to reject some image regions that contain too few or too much edges and thus can not contain the searched object. The particular threshold values are tuned for face detection and in this case the pruning speeds up the processing.
minSize	System.Drawing.Size	Minimum window size. Use Size.Empty for default, where it is set to the size of samples the classifier has been trained on (~20x20 for face detection)
maxSize	System.Drawing.Size	Maxumum window size. Use Size.Empty for default, where the parameter will be ignored.
리턴	Emgu.CV.Structure.MCvAvgComp[]

DisposeObject() 보호된 메소드

Release the HaarCascade Object and all the memory associate with it

protected DisposeObject ( ) : void
리턴	void

HaarCascade() 공개 메소드

Create a HaarCascade object from the specific file

public HaarCascade ( String fileName ) : System
fileName	String	The name of the file that contains the HaarCascade object
리턴	System