Scalar()

查看源码opencv3源码, 发现Scalar_做成了模板类,其中有如下构造函数:可以看到,Scalar是一个由长度为4的数组作为元素构成的结构体,Scalar最多可以存储四个值,没有提供的值默认是0。
使用:
Scalar常用的使用场景如下:

Mat M(7,7,CV_32FC2,Scalar(1,3));

opencv3.4.1源码:

///////////////////////////////// Scalar ////////////////////////////////

template<typename _Tp> inline
Scalar_<_Tp>::Scalar_()
{
    this->val[0] = this->val[1] = this->val[2] = this->val[3] = 0;
}

template<typename _Tp> inline
Scalar_<_Tp>::Scalar_(_Tp v0, _Tp v1, _Tp v2, _Tp v3)
{
    this->val[0] = v0;
    this->val[1] = v1;
    this->val[2] = v2;
    this->val[3] = v3;
}

template<typename _Tp> template<typename _Tp2, int cn> inline
Scalar_<_Tp>::Scalar_(const Vec<_Tp2, cn>& v)
{
    int i;
    for( i = 0; i < (cn < 4 ? cn : 4); i++ )
        this->val[i] = cv::saturate_cast<_Tp>(v.val[i]);
    for( ; i < 4; i++ )
        this->val[i] = 0;
}
//等等
06-14
### Scalar in Programming or Data Context In the context of programming and data, a scalar refers to a single value rather than a collection or array of values. Scalars are fundamental units of data that represent individual elements such as integers, floating-point numbers, characters, or booleans[^1]. In many programming languages, scalars serve as the building blocks for more complex data structures. For example, in C++, scalars can be used directly in operations involving overloaded operators. When performing arithmetic, logical, or comparison operations, scalars interact with each other based on the defined behavior of these operators. Overloaded operators allow custom behavior for standard data types, including scalars, enabling developers to define how operators should behave when applied to user-defined types. In the context of parallel computing, such as CUDA programming, scalars play a critical role in defining thread-level computations. Each thread in a parallel execution model may process a scalar value independently, contributing to the overall computation of a kernel function. The inherent thread model ensures that scalar operations are efficiently distributed across multiple threads, enhancing performance through parallelism[^3]. Additionally, in machine learning frameworks, scalars often represent individual weights or biases within neural networks. During backpropagation, scalar values are updated using gradient descent to minimize the cost function. These updates occur iteratively until convergence, ensuring that each scalar contributes optimally to the network's predictive capabilities[^2]. ```python # Example of scalar usage in Python scalar_value = 42 # A scalar integer floating_scalar = 3.14 # A scalar float # Demonstrating scalar operations result = scalar_value + floating_scalar # Adding two scalar values print(result) ```
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