當(dāng)處理圖片的像素值時(shí)，如變化i=(i/10)*10。沒有必要對每一個(gè)像素值進(jìn)行這個(gè)操作?？梢詣?chuàng)建一個(gè)表，如果是灰度圖像，那么表達(dá)大小是256*常數(shù)2。之后對每一個(gè)像素進(jìn)行查表即可。

運(yùn)行時(shí)間的計(jì)算：

double t = (double)getTickCount();
// do something ...
t = ((double)getTickCount() - t)/getTickFrequency();

在內(nèi)存中，圖像每行的像素存儲(chǔ)一般是是連續(xù)的。

C的遍歷

Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)
{
    // accept only char type matrices
    CV_Assert(I.depth() == CV_8U);
    int channels = I.channels();
    int nRows = I.rows;
    int nCols = I.cols * channels;
    if (I.isContinuous())
    {
        nCols *= nRows;
        nRows = 1;
    }
    int i,j;
    uchar* p;
    for( i = 0; i < nRows; ++i)
    {
        p = I.ptr<uchar>(i);
        for ( j = 0; j < nCols; ++j)
        {
            p[j] = table[p[j]];
        }
    }
    return I;
}

迭代器方法

Mat& ScanImageAndReduceIterator(Mat& I, const uchar* const table)
{
    // accept only char type matrices
    CV_Assert(I.depth() == CV_8U);
    const int channels = I.channels();
    switch(channels)
    {
    case 1:
        {
            MatIterator_<uchar> it, end;
            for( it = I.begin<uchar>(), end = I.end<uchar>(); it != end; ++it)
                *it = table[*it];
            break;
        }
    case 3:
        {
            MatIterator_<Vec3b> it, end;
            for( it = I.begin<Vec3b>(), end = I.end<Vec3b>(); it != end; ++it)
            {
                (*it)[0] = table[(*it)[0]];
                (*it)[1] = table[(*it)[1]];
                (*it)[2] = table[(*it)[2]];
            }
        }
    }
    return I;
}

typedef Vec<uchar, 3> cv::Vec3b
typedef Vec<double, 3> cv::Vec3d
typedef Vec<float, 3> cv::Vec3f
typedef Vec<int, 3> cv::Vec3i
typedef Vec<short, 3> cv::Vec3s
typedef Vec<ushort, 3> cv::Vec3w

On-the-fly address calculation with reference returning

Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar* const table)
{
    // accept only char type matrices
    CV_Assert(I.depth() == CV_8U);
    const int channels = I.channels();
    switch(channels)
    {
    case 1:
        {
            for( int i = 0; i < I.rows; ++i)
                for( int j = 0; j < I.cols; ++j )
                    I.at<uchar>(i,j) = table[I.at<uchar>(i,j)];
            break;
        }
    case 3:
        {
         Mat_<Vec3b> _I = I;
         for( int i = 0; i < I.rows; ++i)
            for( int j = 0; j < I.cols; ++j )
               {
                   _I(i,j)[0] = table[_I(i,j)[0]];
                   _I(i,j)[1] = table[_I(i,j)[1]];
                   _I(i,j)[2] = table[_I(i,j)[2]];
            }
         I = _I;
         break;
        }
    }
    return I;
}

很慢，不過要處理少數(shù)個(gè)數(shù)據(jù)時(shí)候，可以參考。Mat_data type神器呀！

最好的方法

Mat lookUpTable(1, 256, CV_8U);
    uchar* p = lookUpTable.ptr();
    for( int i = 0; i < 256; ++i)
        p[i] = table[i];

LUT(I, lookUpTable, J);

速度最快，因?yàn)?code>This is because the OpenCV library is multi-thread enabled via Intel Threaded Building Blocks.
不過犧牲了一些安全性。