新版的OpenCV 1.1 ,在2008年10月15號發佈,基本上只是優化前一版的內容,刪除了cvcam函式庫,並且提供了一本歐萊里的工具書
http://sourceforge.net/project/showfiles.php?group_id=22870
Notes:
This is OpenCV 1.1pre1, the first intermediate update after 1.0.It features better stereo support, SURF features, better performance of some functions, DirectShow support in highgui etc. It also includes many bug-fixes.
Changes:
>>> New functionality/features: <<<
- General:
* Octave bindings have been added. See interfaces/swig/octave
(for now, Linux only)
* [Windows] OpenCV is now built with VS2005 with SSE2 and OpenMP support included
(if you want to rebuild OpenCV using Express or Standard Edition of VS, use
_make\opencv.vs2005.no_openmp.sln).
* [Windows] Python bindings have been updated to use Python 2.6
* [Windows] cvcam has been removed (as videoInput is now supported by highgui)
- CXCORE, CV, CVAUX:
* Speeded-up Robust Features (SURF), contributed by Liu Liu.
see samples/c/find_obj.cpp and the documentation opencvref_cv.htm
* Many improvements in camera calibration:
- Added stereo camera calibration: cvStereoCalibrate, cvStereoRectify etc.
- Single camera calibration now uses Levenberg-Marquardt method and supports extra flags to
switch on/off optimization of individual camera parameters
- The optional 3rd radial distortion parameter (k3*r^6) is now supported in every calibration-related
function
* 2 stereo correspondence algorithms:
- very fast block matching method by Kurt Konolige
(processes the Tsukuba stereo pair in <10ms on Core2Duo laptop)
- slow but more accurate graph-cut based algorithm by Kolmogorov and Zabin
* Better homography estimation algorithms (RANSAC and LMEDs)
* new C++ template image classes contributed by Daniel Filip (Google inc.). see
opencv/cxcore/include/cvwimage.h
* Fast approximate nearest neighbor search (by Xavier Delacour)
* Codebook method for background/foreground segmentation (by Gary Bradski)
* Sort function (contributed by Shiqi Yu)
* [OpenCV+IPP] Face Detection (cvHaarDetectObjects) now runs much faster (up to 2x faster) when
using IPP 5.3 or higher.
* Much faster (~4x faster) fixed-point variant of cvRemap has been added
- MLL:
* Python bindings for MLL have been added. There are no samples yet.
- HighGUI:
* [Windows, 32bit] Added support for videoInput library. Hence, cvcam is [almost] not needed anymore
* [Windows, 32bit] FFMPEG can now be used for video decoding/encoding via ffopencv*.dll
* [Linux] Added unicap support
* Improved internal video capturing and video encoding APIs
- Documentation:
* OpenCV book has been published (sold separately :) see docs/index.htm)
- New samples (opencv/samples):
* Many Octave samples
* find_obj.cpp (SURF), bgfg_codebook.cpp (Codebook BG/FG segmentation), stereo_calib.cpp
(Stereo calibration and stereo correspondence)
>>> Bug fixes: <<<
Many thanks to everybody who submitted bug reports and/or provided the patches!
* added dma_unlisten to icvCloseCAM_DC1394 (thanks to Victor Benso)
* LMEDs algorithm for cvFindFundamentalMat estimation has been fixed
* Broken debug build of highgui in VS2005/2008 (SF #2056185, SF #1709435)
* cvFindChessboardCorners memory leak and incorrect size checks
(SF #1972896, SF #1910323)
* disabling GTK causes v4l runtime error (SF #2088102)
* cvsetmousecallback bug (SF #2053529)
* libhighgui needed deprecated "img_convert" replacement (SF #2011753)
* Segfault in createsamples caused by uninitialized variable (SF #1977989)
* Data Alignment Issue in bgfg_gaussmix (SF #1961755)
* libpng need to be updated (SF #1952793)
* cvCreateVideoWriter_Win32 - identifier not found (SF #1944254)
* Bug in cvmorph.cpp (SF #1908844)
* dilate (cvDilate) works bogus with default kernel (SF #1887130)
* CvEM non-default constructor declared but not defined (SF #1830346)
* cvFloodFill (in ver 1.0) Hangs (SF #1790565)
* double delete in CvImage (SF #1733437)
* cvFilter2D anchor default value is not working properly (SF #1713779)
* cvCvtColor - Bug? in converting HSV2RGB (SF #1676344)
* Invalid selection of the MKL-dll version in cvUseOptimized() (SF #1633017)
>>> Known issues:
* Borland compiler is not supported (but might work) in this update.
* 64-bit Windows is not supported (but might work) in this update.
* SF bug tracker still contains over 50 records of open bugs. Many of them will be addressed in the next
update.
2008年10月23日 星期四
2008年10月19日 星期日
OpenCV統計應用-CvHistogram直方圖資料結構
CvHistogram可以提供直方圖的計算,並且可以支援多個維度的直方圖設計,但是在繪製直方圖圖形的時候就需要自己用繪圖函式來繪製,CvHistogram算是一個比較複雜的資料結構,由於它在維度小於二的可以用密集的資料結構,CvMatND來設計,可以當它的直方圖維度大於二的時候,就必須要用到稀疏矩陣,CvSparseMat的方式,下面就是用CvHistogram結構計算一維直方圖的例子
CvHistogram一維設計
#include <cv.h>
#include <highgui.h>
#include <stdio.h>
int HistogramBins = 256;
float HistogramRange1[2]={0,255};
float *HistogramRange[1]={
int main()
{
IplImage *Image1;
CvHistogram *Histogram1;
IplImage *HistogramImage1;
Image1=cvLoadImage("Riverbank.jpg",0);
Histogram1 = cvCreateHist(1,
HistogramImage1 = cvCreateImage(cvSize(256,300),8,3);
HistogramImage1->origin=1;
cvCalcHist(
printf("type is : %d\n",Histogram1->type);
printf("Low Bound is : %.f\n",Histogram1->thresh[0][0]);
printf("Up Bound is : %.f\n",Histogram1->thresh[0][1]);
printf("The Bins are : %d\n",((CvMatND *) Histogram1->bins)->dim[0].size);
printf("\nGray Level Values:\n");
for(int i=0;i<HistogramBins;i++)
{
printf("%.f \n",((CvMatND *) Histogram1->bins)->data.fl[i]);
cvLine(HistogramImage1,cvPoint(i,0),cvPoint(i,(int)(cvQueryHistValue_1D(Histogram1,i)/10)),CV_RGB(127,127,127));
}
cvNamedWindow("Histogram",1);
cvNamedWindow("Riverbank",1);
cvShowImage("Riverbank",Image1);
cvShowImage("Histogram",HistogramImage1);
cvWaitKey(0);
}
原始圖片:
執行結果:
跟前面直接用矩陣累加設計出的統計直方圖一樣,到最後還是要自己用繪圖函式自己畫,CvHistogram這個資料結構的特色,它可以設定要用幾個區塊來呈現,這邊設立為256個區塊,而實際上,它可以用比較模糊的方式設定直方圖的區域,也就是可以小於256個的區塊數,是利用切割成n等分的方式,然後它可以自行設定上界(Up Bound)以及下界(Low Bound),在灰階圖裡面數據的範圍為0~255,而CvHistogram資料結構可以設立為上界30,下界200這樣的方式縮小範圍,而0~29,201~266這範圍內的數據將不會被計算,下面是CvHistogram資料結構的內容,分別為CV_HIST_ARRAY及CV_HIST_SPARSE兩種
1.
2.
CvHistogram的type固定都為CV_HIST_MAGIC_VAL這的參數,與上面程式cvCreateHist()所設定的CV_HIST_ARRAY無關,這也許是OpenCV裡面對於CvHistogram這個結構設計不良的地方,而對於CvHistogram資料結構的參數,定義如下
#define CV_HIST_ARRAY 0
#define CV_HIST_SPARSE 1
#define CV_HIST_TREE CV_HIST_SPARSE
因此,它的參數只有CV_HIST_ARRAY的多維矩陣CvMatND以及CV_HIST_SPARSE的稀疏矩陣CvSparseMat資料結構,而它的thresh是放上下界的資料,thresh2是放動態的維度上下界資料,而一般的直方圖數據資料都是放在bins裡面.而CvHistogram內的CvMatND結構則是在做快速初始化,bins以及mat都是使用同一個記憶體空間,而將直方圖資料的提取就要用到cvQueryHistValue_1D()這個函式了.
接著是將直方圖的空間分區塊的方式實作,將它分為50塊,並且上界為30,下界為200
CvHistogram結構區塊與上下界
#include <cv.h>
#include <highgui.h>
#include <stdio.h>
int HistogramBins = 50;
int HistogramBinWidth;
float HistogramRange1[2]={30,200};
float *HistogramRange[1]={
int main()
{
IplImage *Image1;
CvHistogram *Histogram1;
IplImage *HistogramImage1;
CvPoint Point1;
CvPoint Point2;
Image1=cvLoadImage("Riverbank.jpg",0);
Histogram1 = cvCreateHist(1,
HistogramImage1 = cvCreateImage(cvSize(256,300),8,3);
cvSetZero(HistogramImage1);
HistogramImage1->origin=1;
HistogramBinWidth=256/HistogramBins;
printf("The Bolck Width is : %d\n",HistogramBinWidth);
cvCalcHist(
printf("Gray Level Values:\n");
for(int i=0;i<HistogramBins;i++)
{
printf("%.f \n",((CvMatND *) Histogram1->bins)->data.fl[i]);
Point1=cvPoint(i*HistogramBinWidth,0);
Point2=cvPoint((i+1)*HistogramBinWidth,(int)cvQueryHistValue_1D(Histogram1,i)/50);
cvRectangle(HistogramImage1,Point1,Point2,CV_RGB(127,127,127));
}
cvNamedWindow("Histogram",1);
cvNamedWindow("Riverbank",1);
cvShowImage("Riverbank",Image1);
cvShowImage("Histogram",HistogramImage1);
cvWaitKey(0);
}
執行結果:
因此,這邊就用cvRectangle()的方式來表達,對於CvHistogram這個資料結構,為什麼可以任意定義上下界以及區塊的個數呢？因為CvHistogram使用的是Look-up table(LUT)的方式,也就是查表法,開一個256大小空間的陣列,利用比例的縮放,縮放的數據存入Look-up table裡面,在利用索引的方式對應,而使用Look-up table最大的缺點為,無法取得很精確的數據,由於它是被比例縮放過的,因此除了範圍為256的可以求的完整的數值,LUT的用法會在後面使用到.
對於其他維度的直方圖,它的做法如下
三維直方圖擷取
#include <cv.h>
#include <highgui.h>
#include <stdio.h>
int Histogram3DBins[3] = {256,256,256};
float HistogramRange1[6]={0,255,0,255,0,255};
float *HistogramRange[3]={
void Print3DHistogram(CvHistogram *Histogram,int BinSize);
int main()
{
CvHistogram *Histogram1;
IplImage *Image1=cvLoadImage("Riverbank.jpg",1);
IplImage *RedImage=cvCreateImage(cvGetSize(Image1),8,1);
IplImage *GreenImage=cvCreateImage(cvGetSize(Image1),8,1);
IplImage *BlueImage=cvCreateImage(cvGetSize(Image1),8,1);
IplImage *ImageArray[3]={RedImage,GreenImage,BlueImage};
cvSplit(Image1,BlueImage,GreenImage,RedImage,0);
Histogram1 = cvCreateHist(3,Histogram3DBins,CV_HIST_SPARSE,HistogramRange);
cvCalcHist(ImageArray,Histogram1);
printf("3D Historgram Data\n");
Print3DHistogram(Histogram1,256);
cvNamedWindow("Riverbank",1);
cvShowImage("Riverbank",Image1);
cvWaitKey(0);
}
void Print3DHistogram(CvHistogram *Histogram,int BinSize)
{
for(int i=0;i<BinSize;i++)
{
for(int j=0;j<BinSize;j++)
{
for(int k=0;k<BinSize;k++)
{
if(cvQueryHistValue_3D(Histogram,i,j,k)>10)
{
printf("%.f\n",cvQueryHistValue_3D(Histogram,i,j,k));
}
}
}
}
}
執行結果:
上面是三個維度的統計直方圖,分別為R維度,G維度,B維度,並且分別被量化成256等分的區塊,同樣的,也是使用LUT的方法,由於這個統計直方圖非常的大,它所佔的區域為256*256*256的大小,而實際上累積的分佈只有一點點,所以說,對於高維度的統計直方圖就要用到稀疏矩陣,要不然會浪費大量的記憶體空間,但是對於多維度的圖表呈現,OpenCV最多也只能支援到一維的方式,二維的方式可以用OpenGL(glut)呈現,當然高的維度本來就很難用視覺化的方式呈現.
cvCreateHist()
初始化CvHistogram資料結構,可以選擇密集矩陣(CvMatND)CV_HIST_ARRAY以及稀疏矩陣(CvSparseMat)CV_HIST_SPARSE,第一個引數為維度的選擇,第二個引數為要將直方圖切割為多少區塊,第三個引數為選擇cvCreateHist()函數的參數或代號,第四個引數為每一個維度的上下界
cvCreateHist(輸入int型別直方圖維度,輸入int型別直方圖區塊數,輸入參數或代號,輸入每個維度的上下界數據)
cvCalcHist()
計算直方圖累積數據,第一個引數為輸入目標IplImage圖形陣列資料結構,第二個引數輸出為CvHistogram資料結構
cvCalcHist(輸入目標IplImage圖形陣列資料結構,輸出為CvHistogram資料結構)
cvQueryHistValue_1D()
讀取CvHistogram資料結構一維空間的圖形直方圖資料,它在OpenCV的"cvcompat.h"函式庫被定義為
#define cvQueryHistValue_1D( hist, idx0 ) ((float)cvGetReal1D( (hist)->bins, (idx0)))
因此回傳的是float型別的資料,輸入CvHistogram資料結構,以及輸入一維資料結構的Index索引數據
cvQueryHistValue_1D(輸入CvHistogram資料結構,輸入一維int型別數據索引)
cvQueryHistValue_2D()
讀取CvHistogram資料結構二維空間的圖形直方圖資料,它在OpenCV的"cvcompat.h"函式庫被定義為
#define cvQueryHistValue_2D( hist, idx0, idx1 ) ((float)cvGetReal2D( (hist)->bins, (idx0), (idx1)))
回傳float型別的資料,第一個引數為輸入CvHistogram資料結構,第二個引數為輸入二維資料結構的x軸索引數據,第三個引數為y軸索引數據
cvQueryHistValue_2D(輸入CvHistogram資料結構,輸入二維int型別x軸數據索引,輸入二維int型別y軸數據索引)
cvQueryHistValue_3D()
讀取CvHistogram資料結構三維空間的圖形直方圖資料,它在OpenCV的"cvcompat.h"函式庫被定義為
#define cvQueryHistValue_3D( hist, idx0, idx1, idx2 ) ((float)cvGetReal3D( (hist)->bins, (idx0), (idx1), (idx2)))
回傳float型別的資料,第一個引數為輸入CvHistogram資料結構,第二個引數為輸入三維資料結構的x軸索引數據,第三個引數為輸入y軸索引數據,第四個引數為輸入z軸索引數據
cvQueryHistValue_3D(輸入CvHistogram資料結構,輸入三維int型別x軸數據索引,輸入三維int型別y軸數據索引,輸入三維int型別z軸數據索引)
CvHistogram一維設計
#include <cv.h>
#include <highgui.h>
#include <stdio.h>
int HistogramBins = 256;
float HistogramRange1[2]={0,255};
float *HistogramRange[1]={
&HistogramRange1[0]};int main()
{
IplImage *Image1;
CvHistogram *Histogram1;
IplImage *HistogramImage1;
Image1=cvLoadImage("Riverbank.jpg",0);
Histogram1 = cvCreateHist(1,
&HistogramBins,CV_HIST_ARRAY,HistogramRange);HistogramImage1 = cvCreateImage(cvSize(256,300),8,3);
HistogramImage1->origin=1;
cvCalcHist(
&Image1,Histogram1);printf("type is : %d\n",Histogram1->type);
printf("Low Bound is : %.f\n",Histogram1->thresh[0][0]);
printf("Up Bound is : %.f\n",Histogram1->thresh[0][1]);
printf("The Bins are : %d\n",((CvMatND *) Histogram1->bins)->dim[0].size);
printf("\nGray Level Values:\n");
for(int i=0;i<HistogramBins;i++)
{
printf("%.f \n",((CvMatND *) Histogram1->bins)->data.fl[i]);
cvLine(HistogramImage1,cvPoint(i,0),cvPoint(i,(int)(cvQueryHistValue_1D(Histogram1,i)/10)),CV_RGB(127,127,127));
}
cvNamedWindow("Histogram",1);
cvNamedWindow("Riverbank",1);
cvShowImage("Riverbank",Image1);
cvShowImage("Histogram",HistogramImage1);
cvWaitKey(0);
}
原始圖片:
執行結果:
跟前面直接用矩陣累加設計出的統計直方圖一樣,到最後還是要自己用繪圖函式自己畫,CvHistogram這個資料結構的特色,它可以設定要用幾個區塊來呈現,這邊設立為256個區塊,而實際上,它可以用比較模糊的方式設定直方圖的區域,也就是可以小於256個的區塊數,是利用切割成n等分的方式,然後它可以自行設定上界(Up Bound)以及下界(Low Bound),在灰階圖裡面數據的範圍為0~255,而CvHistogram資料結構可以設立為上界30,下界200這樣的方式縮小範圍,而0~29,201~266這範圍內的數據將不會被計算,下面是CvHistogram資料結構的內容,分別為CV_HIST_ARRAY及CV_HIST_SPARSE兩種
1.
2.
CvHistogram的type固定都為CV_HIST_MAGIC_VAL這的參數,與上面程式cvCreateHist()所設定的CV_HIST_ARRAY無關,這也許是OpenCV裡面對於CvHistogram這個結構設計不良的地方,而對於CvHistogram資料結構的參數,定義如下
#define CV_HIST_ARRAY 0
#define CV_HIST_SPARSE 1
#define CV_HIST_TREE CV_HIST_SPARSE
因此,它的參數只有CV_HIST_ARRAY的多維矩陣CvMatND以及CV_HIST_SPARSE的稀疏矩陣CvSparseMat資料結構,而它的thresh是放上下界的資料,thresh2是放動態的維度上下界資料,而一般的直方圖數據資料都是放在bins裡面.而CvHistogram內的CvMatND結構則是在做快速初始化,bins以及mat都是使用同一個記憶體空間,而將直方圖資料的提取就要用到cvQueryHistValue_1D()這個函式了.
接著是將直方圖的空間分區塊的方式實作,將它分為50塊,並且上界為30,下界為200
CvHistogram結構區塊與上下界
#include <cv.h>
#include <highgui.h>
#include <stdio.h>
int HistogramBins = 50;
int HistogramBinWidth;
float HistogramRange1[2]={30,200};
float *HistogramRange[1]={
&HistogramRange1[0]};int main()
{
IplImage *Image1;
CvHistogram *Histogram1;
IplImage *HistogramImage1;
CvPoint Point1;
CvPoint Point2;
Image1=cvLoadImage("Riverbank.jpg",0);
Histogram1 = cvCreateHist(1,
&HistogramBins,CV_HIST_ARRAY,HistogramRange);HistogramImage1 = cvCreateImage(cvSize(256,300),8,3);
cvSetZero(HistogramImage1);
HistogramImage1->origin=1;
HistogramBinWidth=256/HistogramBins;
printf("The Bolck Width is : %d\n",HistogramBinWidth);
cvCalcHist(
&Image1,Histogram1);printf("Gray Level Values:\n");
for(int i=0;i<HistogramBins;i++)
{
printf("%.f \n",((CvMatND *) Histogram1->bins)->data.fl[i]);
Point1=cvPoint(i*HistogramBinWidth,0);
Point2=cvPoint((i+1)*HistogramBinWidth,(int)cvQueryHistValue_1D(Histogram1,i)/50);
cvRectangle(HistogramImage1,Point1,Point2,CV_RGB(127,127,127));
}
cvNamedWindow("Histogram",1);
cvNamedWindow("Riverbank",1);
cvShowImage("Riverbank",Image1);
cvShowImage("Histogram",HistogramImage1);
cvWaitKey(0);
}
執行結果:
因此,這邊就用cvRectangle()的方式來表達,對於CvHistogram這個資料結構,為什麼可以任意定義上下界以及區塊的個數呢？因為CvHistogram使用的是Look-up table(LUT)的方式,也就是查表法,開一個256大小空間的陣列,利用比例的縮放,縮放的數據存入Look-up table裡面,在利用索引的方式對應,而使用Look-up table最大的缺點為,無法取得很精確的數據,由於它是被比例縮放過的,因此除了範圍為256的可以求的完整的數值,LUT的用法會在後面使用到.
對於其他維度的直方圖,它的做法如下
三維直方圖擷取
#include <cv.h>
#include <highgui.h>
#include <stdio.h>
int Histogram3DBins[3] = {256,256,256};
float HistogramRange1[6]={0,255,0,255,0,255};
float *HistogramRange[3]={
&HistogramRange1[0],&HistogramRange1[2],&HistogramRange1[4]};void Print3DHistogram(CvHistogram *Histogram,int BinSize);
int main()
{
CvHistogram *Histogram1;
IplImage *Image1=cvLoadImage("Riverbank.jpg",1);
IplImage *RedImage=cvCreateImage(cvGetSize(Image1),8,1);
IplImage *GreenImage=cvCreateImage(cvGetSize(Image1),8,1);
IplImage *BlueImage=cvCreateImage(cvGetSize(Image1),8,1);
IplImage *ImageArray[3]={RedImage,GreenImage,BlueImage};
cvSplit(Image1,BlueImage,GreenImage,RedImage,0);
Histogram1 = cvCreateHist(3,Histogram3DBins,CV_HIST_SPARSE,HistogramRange);
cvCalcHist(ImageArray,Histogram1);
printf("3D Historgram Data\n");
Print3DHistogram(Histogram1,256);
cvNamedWindow("Riverbank",1);
cvShowImage("Riverbank",Image1);
cvWaitKey(0);
}
void Print3DHistogram(CvHistogram *Histogram,int BinSize)
{
for(int i=0;i<BinSize;i++)
{
for(int j=0;j<BinSize;j++)
{
for(int k=0;k<BinSize;k++)
{
if(cvQueryHistValue_3D(Histogram,i,j,k)>10)
{
printf("%.f\n",cvQueryHistValue_3D(Histogram,i,j,k));
}
}
}
}
}
執行結果:
上面是三個維度的統計直方圖,分別為R維度,G維度,B維度,並且分別被量化成256等分的區塊,同樣的,也是使用LUT的方法,由於這個統計直方圖非常的大,它所佔的區域為256*256*256的大小,而實際上累積的分佈只有一點點,所以說,對於高維度的統計直方圖就要用到稀疏矩陣,要不然會浪費大量的記憶體空間,但是對於多維度的圖表呈現,OpenCV最多也只能支援到一維的方式,二維的方式可以用OpenGL(glut)呈現,當然高的維度本來就很難用視覺化的方式呈現.
cvCreateHist()
初始化CvHistogram資料結構,可以選擇密集矩陣(CvMatND)CV_HIST_ARRAY以及稀疏矩陣(CvSparseMat)CV_HIST_SPARSE,第一個引數為維度的選擇,第二個引數為要將直方圖切割為多少區塊,第三個引數為選擇cvCreateHist()函數的參數或代號,第四個引數為每一個維度的上下界
cvCreateHist(輸入int型別直方圖維度,輸入int型別直方圖區塊數,輸入參數或代號,輸入每個維度的上下界數據)
cvCalcHist()
計算直方圖累積數據,第一個引數為輸入目標IplImage圖形陣列資料結構,第二個引數輸出為CvHistogram資料結構
cvCalcHist(輸入目標IplImage圖形陣列資料結構,輸出為CvHistogram資料結構)
cvQueryHistValue_1D()
讀取CvHistogram資料結構一維空間的圖形直方圖資料,它在OpenCV的"cvcompat.h"函式庫被定義為
#define cvQueryHistValue_1D( hist, idx0 ) ((float)cvGetReal1D( (hist)->bins, (idx0)))
因此回傳的是float型別的資料,輸入CvHistogram資料結構,以及輸入一維資料結構的Index索引數據
cvQueryHistValue_1D(輸入CvHistogram資料結構,輸入一維int型別數據索引)
cvQueryHistValue_2D()
讀取CvHistogram資料結構二維空間的圖形直方圖資料,它在OpenCV的"cvcompat.h"函式庫被定義為
#define cvQueryHistValue_2D( hist, idx0, idx1 ) ((float)cvGetReal2D( (hist)->bins, (idx0), (idx1)))
回傳float型別的資料,第一個引數為輸入CvHistogram資料結構,第二個引數為輸入二維資料結構的x軸索引數據,第三個引數為y軸索引數據
cvQueryHistValue_2D(輸入CvHistogram資料結構,輸入二維int型別x軸數據索引,輸入二維int型別y軸數據索引)
cvQueryHistValue_3D()
讀取CvHistogram資料結構三維空間的圖形直方圖資料,它在OpenCV的"cvcompat.h"函式庫被定義為
#define cvQueryHistValue_3D( hist, idx0, idx1, idx2 ) ((float)cvGetReal3D( (hist)->bins, (idx0), (idx1), (idx2)))
回傳float型別的資料,第一個引數為輸入CvHistogram資料結構,第二個引數為輸入三維資料結構的x軸索引數據,第三個引數為輸入y軸索引數據,第四個引數為輸入z軸索引數據
cvQueryHistValue_3D(輸入CvHistogram資料結構,輸入三維int型別x軸數據索引,輸入三維int型別y軸數據索引,輸入三維int型別z軸數據索引)
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