OpenCV-------Iplimage结构详解

绝地灬酷狼 2022-05-14 04:27 226阅读 0赞

# 文章来源：[点击打开链接][Link 1] #

## IplImage介绍 ##

typedef struct _IplImage
    {
        int  nSize;             /* sizeof(IplImage) */
        int  ID;                /* version (=0)*/
        int  nChannels;         /* Most of OpenCV functions support 1,2,3 or 4 channels */
        int  alphaChannel;      /* Ignored by OpenCV */
        int  depth;             /* Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S,
                                   IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported.  */
        char colorModel[4];     /* Ignored by OpenCV */
        char channelSeq[4];     /* ditto */
        int  dataOrder;         /* 0 - interleaved color channels, 1 - separate color channels.
                                   cvCreateImage can only create interleaved images */
        int  origin;            /* 0 - top-left origin,
                                   1 - bottom-left origin (Windows bitmaps style).  */
        int  align;             /* Alignment of image rows (4 or 8).
                                   OpenCV ignores it and uses widthStep instead.    */
        int  width;             /* Image width in pixels.                           */
        int  height;            /* Image height in pixels.                          */
        struct _IplROI *roi;    /* Image ROI. If NULL, the whole image is selected. */
        struct _IplImage *maskROI;      /* Must be NULL. */
        void  *imageId;                 /* "           " */
        struct _IplTileInfo *tileInfo;  /* "           " */
        int  imageSize;         /* Image data size in bytes
                                   (==image->height*image->widthStep
                                   in case of interleaved data)*/
        char *imageData;        /* Pointer to aligned image data.         */
        int  widthStep;         /* Size of aligned image row in bytes.    */
        int  BorderMode[4];     /* Ignored by OpenCV.                     */
        int  BorderConst[4];    /* Ditto.                                 */
        char *imageDataOrigin;  /* Pointer to very origin of image data
                                   (not necessarily aligned) -
                                   needed for correct deallocation */
    }
    IplImage;
      
       
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depth变量：像素的深度。深度值有：

#define IPL_DEPTH_1U     1
    #define IPL_DEPTH_8U     8
    #define IPL_DEPTH_16U   16
    #define IPL_DEPTH_32F   32
    
    #define IPL_DEPTH_8S  (IPL_DEPTH_SIGN| 8)
    #define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16)
    #define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32)
      
       
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通俗一点讲就是像素的取值大小，例如：IPL\_DEPTH\_8U 即代表有8位数据，最大值256，即一个像素的颜色等级分为256种。从0到256不断加深。

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nChannels变量：图像通道（通道的概念之前有介绍）。  
彩色图像的排列方式：  
彩色图像一般是由3通道表示。3通道表示一个像素点有三个值，分别对应R,G,B.RGB决定该像素点的颜色。

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一下其他的重要参数：  
imageData 包含一个指向第一行图像数据的指针。  
widthStep是指一个排列图像行的大小，以字节为单位。  
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## 访问图像数据 ##

实例：分别打印一个彩色图像和R,G,B三个单通道的图像

int main()
    {
        IplImage* img;
        img = cvLoadImage("F:\\Opencv\\素材\\楪析.jpg");
        IplImage* b_img = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 1);
        IplImage* g_img = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 1);
        IplImage* r_img = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 1);
        int x, y;
        for (y = 0; y < img->height; y++)
        {
            unsigned char* prow = (unsigned char*)(img->imageData + y*img->widthStep);
            unsigned char* brow = (unsigned char*)(b_img->imageData + y*b_img->widthStep);
            unsigned char* grow = (unsigned char*)(g_img->imageData + y*g_img->widthStep);
            unsigned char* rrow = (unsigned char*)(r_img->imageData + y*r_img->widthStep);
    
            for (x = 0; x < img->width; x++)
            {
                brow[x] = prow[3 * x + 0];
                grow[x] = prow[3 * x + 1];
                rrow[x] = prow[3 * x + 2];
            }
        }
    
        cvNamedWindow("1");
        cvShowImage("1", img);
    
        cvNamedWindow("b");
        cvShowImage("b", b_img);
    
        cvNamedWindow("r");
        cvShowImage("r", r_img);
    
        cvNamedWindow("g");
        cvShowImage("g", g_img);
    
        cvWaitKey(0);
        return 0;
    
    
    }
      
       
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## 对ROI和widthStep的补充 ##

ROI全称：region of interest,即：感兴趣的区域。使用ROI可以对图像的部分区域进行操作而不是对图像额整个区域进行运算，可以提高计算机视觉代码的执行速度。  
例：用imageROI来增加某范围的像素。

int main()
    {
        IplImage* img;
        img = cvLoadImage("F:\\Opencv\\素材\\楪析.jpg");
        CvRect rect;
        rect.x = 350;
        rect.y = 102;
        rect.width = 166;
        rect.height = 141;
        cvSetImageROI(img, rect);
    
        cvAddS(img, cvScalar(150), img);
    
        cvResetImageROI(img);
        cvNamedWindow("1");
        cvShowImage("1", img);
    
        cvWaitKey(0);
    
        return 0;
    }
      
       
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介绍几个主要函数：  
cvSetImageROI()和cvResetImageROI()这两个函数用来设置和取消ROI，  
void cvSetImageROI(IplImage\* image,CvRect rect);  
需要传递一个图像指针和矩阵，这个矩阵

typedef struct CvRect
    {
        int x;
        int y;
        int width;
        int height;
    }
    CvRect;
      
       
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x,y是ROI图像起始点的坐标，width和height是ROI图像的宽度和高度。  
图像指针是指我们选取ROI区域的母图像。

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我们可以使用widthStep达到同样的效果，  
首先我们要创建一个CvRect结构体,设置图像起点和大小。  
然后创建一个图像头，让它的width和height和之前创建CvRect结构体的width和height相同，并且让它的通道和像素深度与母图像的相同。  
在使图像头的origin（起点）和widthStep（步长）和母图像相同，  
可以理解为创建一个大小与母图像相等CvRect结构体指定的图像区域有颜色，其他地方是透明的图像覆盖在母图像上面。例程如下：

int main()
    {
        IplImage* interest_img = cvLoadImage("F:\\Opencv\\素材\\楪析.jpg");
        CvRect interest_rect = cvRect(350, 102, 166, 141);
        IplImage* sub_img = cvCreateImageHeader
            (
            cvSize(interest_rect.width, interest_rect.height),
            interest_img->depth,
            interest_img->nChannels
            );
        sub_img->origin = interest_img->origin;
        sub_img->widthStep = interest_img->widthStep;
        sub_img->imageData = interest_img->imageData + 
            interest_img->widthStep*interest_rect.y +
            interest_img->nChannels*interest_rect.x;
        cvAddS(sub_img, cvScalar(150), sub_img);
    
        cvNamedWindow("interest_img");
        cvNamedWindow("sub_img");
        cvShowImage("interest_img", interest_img);
        cvShowImage("sub_img", sub_img);
        cvResetImageROI(sub_img);
        cvWaitKey(0);
        cvDestroyWindow("interest_img");
        cvDestroyWindow("sub_img");
        interest_img = NULL;
        sub_img = NULL;
    
        return 0;
    }
      
       
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看起来设置和重置ROI更方便一些，为什么还要使用widthStep？原因在于有些时候在处理的过程中，想在操作过程中设置和保持一幅图像的多个子区域处于活动状态，但是ROI只能串行处理并且必须不断地设置和重置。

### widthStep与width\*nChannels是否相等： ###

其中IPL\_DEPTH\_SIGN是0x800000000,align大小为CV\_DEFAULT\_IMAGE\_ROW\_ALIGN，定义为4，由此可见当图像为3\*5，通道为3通道时，widthStep为12；同时经过分析可以知道事实上是widht\*nChannels按4整数倍内存对齐的结果。  
如宽度为3、通道数为3的图像，每一行需要的实际内存长度为3\*3，为了内存对齐，OpenCV会在每行末尾自动补上3个字节的内存，内存初始化都为0，所以widthStep变为了12。  
widthStep大小对IplImage极为重要，在array.cpp中，我们可以找到如下代码行：  
image->imageSize = image->widthStep \* image->height;  
img->imageData = img->imageDataOrigin =  
(char\*)cvAlloc( (size\_t)img->imageSize );  
可见widthStep直接影响到imageData的数据长度。

[Link 1]: https://blog.csdn.net/hx1298234467/article/details/48376175