OpenCV学习笔记--图像的载入和显示

根据http://blog.csdn.net/poem_qianmo/article/details/20537737内容自己在vs动手操作了一下，文章内容基本出自该文，并写了一点自己的理解~

1.imread函数

imread函数，载入图片，转到定义处，第一个参数为文件名，第二个flag为加载图片的颜色类型，

Mat imread( const String& filename, int flags = IMREAD_COLOR );

转到IMREAD_COLOR的定义处，可以看到

//! Imread flags
enum ImreadModes {
       IMREAD_UNCHANGED            = -1, //!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped).
       IMREAD_GRAYSCALE            = 0,  //!< If set, always convert image to the single channel grayscale image.
       IMREAD_COLOR                = 1,  //!< If set, always convert image to the 3 channel BGR color image.
       IMREAD_ANYDEPTH             = 2,  //!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit.
       IMREAD_ANYCOLOR             = 4,  //!< If set, the image is read in any possible color format.
       IMREAD_LOAD_GDAL            = 8,  //!< If set, use the gdal driver for loading the image.
       IMREAD_REDUCED_GRAYSCALE_2  = 16, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2.
       IMREAD_REDUCED_COLOR_2      = 17, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2.
       IMREAD_REDUCED_GRAYSCALE_4  = 32, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4.
       IMREAD_REDUCED_COLOR_4      = 33, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4.
       IMREAD_REDUCED_GRAYSCALE_8  = 64, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8.
       IMREAD_REDUCED_COLOR_8      = 65  //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8.
     };

flags默认值为1，根据注释得到当调用时缺少参数的时候，默认加载图片为三通道的彩色图像，关于通道的理解，参考了这篇文章--学习OpenCV2——Mat之通道的理解

flags的值为2的时候，即当载入图片深度为16位/32位的图片，则载入对应深度的图片，否则载入8位图像。关于深度，深度为该图片存储每个像素所用的位数，图像深度确定彩色图像的每个像素可能有的颜色数,或者确定灰度图像的每个像素可能有的灰度级数。（若深度为n，则可能有的颜色数为2的n次方/灰度数目为2的n次方）

2.namedWindow函数

namedWindow函数，创建窗口，转到定义处，第一个参数为创建的窗口名，第二个参数flag为窗口类型，并转到WINDOW_AUTOSIZE的定义处

void namedWindow(const String& winname, int flags = WINDOW_AUTOSIZE);

//! Flags for cv::namedWindow
enum WindowFlags {
       WINDOW_NORMAL     = 0x00000000, //!< the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size.
       WINDOW_AUTOSIZE   = 0x00000001, //!< the user cannot resize the window, the size is constrainted by the image displayed.
       WINDOW_OPENGL     = 0x00001000, //!< window with opengl support.

       WINDOW_FULLSCREEN = 1,          //!< change the window to fullscreen.
       WINDOW_FREERATIO  = 0x00000100, //!< the image expends as much as it can (no ratio constraint).
       WINDOW_KEEPRATIO  = 0x00000000  //!< the ratio of the image is respected.
     };

当flags值为WINDOW_NORMAL时可以重新定义窗口的大小，WINDOW_AUTOSIZE时为自适应大小，且不能重新定义大小

3.imshow函数

imshow函数，用于在指定的窗口中显示图像，第一个参数为窗口的名字，如果窗口是用CV_WINDOW_AUTOSIZE（默认值）标志创建的，那么显示图像原始大小。否则，将图像进行缩放以适合窗口。而imshow 函数缩放图像，取决于图像的深度；第二个类型为InputArray的参数mat，转到其定义处

void imshow(const String& winname, InputArray mat);

InputArray为自定义类型，查看其结构

class CV_EXPORTS _InputArray
{
public:
    enum {
        KIND_SHIFT = 16,
        FIXED_TYPE = 0x8000 << KIND_SHIFT,
        FIXED_SIZE = 0x4000 << KIND_SHIFT,
        KIND_MASK = 31 << KIND_SHIFT,

        NONE              = 0 << KIND_SHIFT,
        MAT               = 1 << KIND_SHIFT,
        MATX              = 2 << KIND_SHIFT,
        STD_VECTOR        = 3 << KIND_SHIFT,
        STD_VECTOR_VECTOR = 4 << KIND_SHIFT,
        STD_VECTOR_MAT    = 5 << KIND_SHIFT,
        EXPR              = 6 << KIND_SHIFT,
        OPENGL_BUFFER     = 7 << KIND_SHIFT,
        CUDA_HOST_MEM     = 8 << KIND_SHIFT,
        CUDA_GPU_MAT      = 9 << KIND_SHIFT,
        UMAT              =10 << KIND_SHIFT,
        STD_VECTOR_UMAT   =11 << KIND_SHIFT,
        STD_BOOL_VECTOR   =12 << KIND_SHIFT,
        STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT
    };

    _InputArray();
    _InputArray(int _flags, void* _obj);
    _InputArray(const Mat& m);
    _InputArray(const MatExpr& expr);
    _InputArray(const std::vector<Mat>& vec);
    template<typename _Tp> _InputArray(const Mat_<_Tp>& m);
    template<typename _Tp> _InputArray(const std::vector<_Tp>& vec);
    _InputArray(const std::vector<bool>& vec);
    template<typename _Tp> _InputArray(const std::vector<std::vector<_Tp> >& vec);
    template<typename _Tp> _InputArray(const std::vector<Mat_<_Tp> >& vec);
    template<typename _Tp> _InputArray(const _Tp* vec, int n);
    template<typename _Tp, int m, int n> _InputArray(const Matx<_Tp, m, n>& matx);
    _InputArray(const double& val);
    _InputArray(const cuda::GpuMat& d_mat);
    _InputArray(const std::vector<cuda::GpuMat>& d_mat_array);
    _InputArray(const ogl::Buffer& buf);
    _InputArray(const cuda::HostMem& cuda_mem);
    template<typename _Tp> _InputArray(const cudev::GpuMat_<_Tp>& m);
    _InputArray(const UMat& um);
    _InputArray(const std::vector<UMat>& umv);

    Mat getMat(int idx=-1) const;
    Mat getMat_(int idx=-1) const;
    UMat getUMat(int idx=-1) const;
    void getMatVector(std::vector<Mat>& mv) const;
    void getUMatVector(std::vector<UMat>& umv) const;
    void getGpuMatVector(std::vector<cuda::GpuMat>& gpumv) const;
    cuda::GpuMat getGpuMat() const;
    ogl::Buffer getOGlBuffer() const;

    int getFlags() const;
    void* getObj() const;
    Size getSz() const;

    int kind() const;
    int dims(int i=-1) const;
    int cols(int i=-1) const;
    int rows(int i=-1) const;
    Size size(int i=-1) const;
    int sizend(int* sz, int i=-1) const;
    bool sameSize(const _InputArray& arr) const;
    size_t total(int i=-1) const;
    int type(int i=-1) const;
    int depth(int i=-1) const;
    int channels(int i=-1) const;
    bool isContinuous(int i=-1) const;
    bool isSubmatrix(int i=-1) const;
    bool empty() const;
    void copyTo(const _OutputArray& arr) const;
    void copyTo(const _OutputArray& arr, const _InputArray & mask) const;
    size_t offset(int i=-1) const;
    size_t step(int i=-1) const;
    bool isMat() const;
    bool isUMat() const;
    bool isMatVector() const;
    bool isUMatVector() const;
    bool isMatx() const;
    bool isVector() const;
    bool isGpuMatVector() const;
    ~_InputArray();

protected:
    int flags;
    void* obj;
    Size sz;

    void init(int _flags, const void* _obj);
    void init(int _flags, const void* _obj, Size _sz);
};

emmm可以看到，_InputArray类的里面首先定义了一个枚举，然后是各类的模板类型和一些方法，(并不想看)并不看懂(xxxx)，不过在mat.hpp在_InputArray类前有一段介绍，大致理解为用来存储Mat对象的容器

/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions.

It is defined as:
@code
    typedef const _InputArray& InputArray;
@endcode
where _InputArray is a class that can be constructed from `Mat`, `Mat_<T>`, `Matx<T, m, n>`,
`std::vector<T>`, `std::vector<std::vector<T> >` or `std::vector<Mat>`. It can also be constructed
from a matrix expression.

Since this is mostly implementation-level class, and its interface may change in future versions, we
do not describe it in details. There are a few key things, though, that should be kept in mind:

-   When you see in the reference manual or in OpenCV source code a function that takes
    InputArray, it means that you can actually pass `Mat`, `Matx`, `vector<T>` etc. (see above the
    complete list).
-   Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or
    simply cv::Mat() as you probably did before).
-   The class is designed solely for passing parameters. That is, normally you *should not*
    declare class members, local and global variables of this type.
-   If you want to design your own function or a class method that can operate of arrays of
    multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside
    a function you should use _InputArray::getMat() method to construct a matrix header for the
    array (without copying data). _InputArray::kind() can be used to distinguish Mat from
    `vector<>` etc., but normally it is not needed.

Here is how you can use a function that takes InputArray :
@code
    std::vector<Point2f> vec;
    // points or a circle
    for( int i = 0; i < 30; i++ )
        vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)),
                              (float)(100 - 30*sin(i*CV_PI*2/5))));
    cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20));
@endcode
That is, we form an STL vector containing points, and apply in-place affine transformation to the
vector using the 2x3 matrix created inline as `Matx<float, 2, 3>` instance.

Here is how such a function can be implemented (for simplicity, we implement a very specific case of
it, according to the assertion statement inside) :
@code
    void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m)
    {
        // get Mat headers for input arrays. This is O(1) operation,
        // unless _src and/or _m are matrix expressions.
        Mat src = _src.getMat(), m = _m.getMat();
        CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) );

        // [re]create the output array so that it has the proper size and type.
        // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize.
        _dst.create(src.size(), src.type());
        Mat dst = _dst.getMat();

        for( int i = 0; i < src.rows; i++ )
            for( int j = 0; j < src.cols; j++ )
            {
                Point2f pt = src.at<Point2f>(i, j);
                dst.at<Point2f>(i, j) = Point2f(m.at<float>(0, 0)*pt.x +
                                                m.at<float>(0, 1)*pt.y +
                                                m.at<float>(0, 2),
                                                m.at<float>(1, 0)*pt.x +
                                                m.at<float>(1, 1)*pt.y +
                                                m.at<float>(1, 2));
            }
    }
@endcode
There is another related type, InputArrayOfArrays, which is currently defined as a synonym for
InputArray:
@code
    typedef InputArray InputArrayOfArrays;
@endcode
It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate
synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation
level their use is similar, but _InputArray::getMat(idx) should be used to get header for the
idx-th component of the outer vector and _InputArray::size().area() should be used to find the
number of components (vectors/matrices) of the outer vector.
 */

4.imwrite函数

imwirte函数，输出图像到文件，第一个参数为图片名字，第二个为InputArray类型的图片 InputArray类型在上面有介绍。第三个参数const vector<int>&类型的params，表示为特定格式保存的参数编码，它有默认值vector<int>()，所以一般情况下不需要填写。

bool imwrite( const String& filename, InputArray img,
              const std::vector<int>& params = std::vector<int>());

　　第三个参数：

　　　　1. 对于JPEG格式的图片，这个参数表示从0到100的图片质量（CV_IMWRITE_JPEG_QUALITY），默认值是95.

　　　　2.对于PNG格式的图片，这个参数表示压缩级别（CV_IMWRITE_PNG_COMPRESSION）从0到9。较高的值意味着更小的尺寸和更长的压缩时间，而默认值是3

　　　　3. 对于PPM，PGM，或PBM格式的图片，这个参数表示一个二进制格式标志（CV_IMWRITE_PXM_BINARY），取值为0或1，而默认值是1。