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I could offer you a generic for_each loop, you could design you own generic algorithms by your own needs.

/**
 *@brief apply stl like for_each algorithm on a channel
 *
 * @param T : the type of the channel(ex, uchar, float, double and so on)
 * @param func : Unary function that accepts an element in the range as argument
 *
 *@return :
 *  return func
 */
template<typename T, typename UnaryFunc, typename Mat>
inline UnaryFunc for_each_channels(Mat &&input, UnaryFunc func)
{
    int rows = input.rows;
    int cols = input.cols;

    if(input.isContinuous()){
        cols = input.total() * input.channels();
        rows = 1;
    }

    for(int row = 0; row != rows; ++row){
        auto input_ptr = input.template ptr<T>(row);
        for(int col = 0; col != cols; ++col){
            func(input_ptr[col]);
        }
    }

    return func;
}

There are still rooms to speed up the algorithms, like vectorize?(I don't know how to do it yet) Multithread, although I can develop it with std::thread, but don't know how to make a thread pool, without a decent thread pool the parallel for_each algorithm is not ready yet.

I use && to make sure it could accept cv::Mat& and cv::Mat const&. This link explain the principle of the algorithm with details(only principle) generic algorithm

I could offer you a generic for_each loop, you could design you own generic algorithms by your own needs.

/**
 *@brief apply stl like for_each algorithm on a channel
 *
 * @param T : the type of the channel(ex, uchar, float, double and so on)
 * @param func : Unary function that accepts an element in the range as argument
 *
 *@return :
 *  return func
 */
template<typename T, typename UnaryFunc, typename Mat>
inline UnaryFunc for_each_channels(Mat &&input, UnaryFunc func)
{
    int rows = input.rows;
    int cols = input.cols;

    if(input.isContinuous()){
        cols = input.total() * input.channels();
        rows = 1;
    }

    for(int row = 0; row != rows; ++row){
        auto input_ptr = input.template ptr<T>(row);
        for(int col = 0; col != cols; ++col){
            func(input_ptr[col]);
        }
    }

    return func;
}

for_each_channels<T>(input, [](T &data){ data = std::tanh(data); })

There are still rooms to speed up the algorithms, like vectorize?(I don't know how to do it yet) Multithread, although I can develop it with std::thread, but don't know how to make a thread pool, without a decent thread pool the parallel for_each algorithm is not ready yet.yet.Although other language do not support template, I think this kind of generic algorithm could speed up the development speed of the openCV source and make the life of the c++ programmers easier.

I use && to make sure it could accept cv::Mat& and cv::Mat const&. This link explain the principle of the algorithm with details(only principle) generic algorithm