Mat对象：图像文件的内存数据对象
读取为 Mat 对象
读取图像位Mat对象，获取图像的相关信息
#include
#include
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
Mat src = imread("f:/temp/images/lena.jpg", IMREAD_GRAYSCALE);
if (src.empty())
{
printf("Could not find the image!\n");
return -1;
}
namedWindow("002-demo", WINDOW_AUTOSIZE);
imshow("002-demo", src);
// 获取图像的宽度
int width = src.cols;
// 获取图像的高度
int height = src.rows;
// 获取图像的通道数
int dim = src.channels();
int depth = src.depth();
int type = src.type();
if (type == CV_8UC1)
{
printf("type：CV_8UC1\n");
}
// 这里打印出来depth和type有时候为0，并不代表深度为0，而是代表一个枚举类型的值
printf("width: %d, height: %d, dim: %d, depth: %d, type: %d\n", width, height, dim, depth, type);
waitKey(0);
destroyAllWindows();
return 0;
} 图像的类型与结构信息：
#ifndef OPENCV_CORE_HAL_INTERFACE_H
#define OPENCV_CORE_HAL_INTERFACE_H
//! @addtogroup core_hal_interface
//! @{
//! @name Return codes
//! @{
#define CV_HAL_ERROR_OK 0
#define CV_HAL_ERROR_NOT_IMPLEMENTED 1
#define CV_HAL_ERROR_UNKNOWN -1
//! @}
#ifdef __cplusplus
#include
#else
#include
#include
#endif
//! @name Data types
//! primitive types
//! - schar - signed 1 byte integer
//! - uchar - unsigned 1 byte integer
//! - short - signed 2 byte integer
//! - ushort - unsigned 2 byte integer
//! - int - signed 4 byte integer
//! - uint - unsigned 4 byte integer
//! - int64 - signed 8 byte integer
//! - uint64 - unsigned 8 byte integer
//! @{
#if !defined _MSC_VER && !defined __BORLANDC__
# if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__
# include
# ifdef __NEWLIB__
typedef unsigned int uint;
# else
typedef std::uint32_t uint;
# endif
# else
# include
typedef uint32_t uint;
# endif
#else
typedef unsigned uint;
#endif
typedef signed char schar;
#ifndef __IPL_H__
typedef unsigned char uchar;
typedef unsigned short ushort;
#endif
#if defined _MSC_VER || defined __BORLANDC__
typedef __int64 int64;
typedef unsigned __int64 uint64;
# define CV_BIG_INT(n) n##I64
# define CV_BIG_UINT(n) n##UI64
#else
typedef int64_t int64;
typedef uint64_t uint64;
# define CV_BIG_INT(n) n##LL
# define CV_BIG_UINT(n) n##ULL
#endif
#define CV_USRTYPE1 (void)"CV_USRTYPE1 support has been dropped in OpenCV 4.0"
#define CV_CN_MAX 512
#define CV_CN_SHIFT 3
#define CV_DEPTH_MAX (1 << CV_CN_SHIFT)
#define CV_8U 0
#define CV_8S 1
#define CV_16U 2
#define CV_16S 3
#define CV_32S 4
#define CV_32F 5
#define CV_64F 6
#define CV_16F 7
#define CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1)
#define CV_MAT_DEPTH(flags) ((flags) & CV_MAT_DEPTH_MASK)
#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT))
#define CV_MAKE_TYPE CV_MAKETYPE
#define CV_8UC1 CV_MAKETYPE(CV_8U,1)
#define CV_8UC2 CV_MAKETYPE(CV_8U,2)
#define CV_8UC3 CV_MAKETYPE(CV_8U,3)
#define CV_8UC4 CV_MAKETYPE(CV_8U,4)
#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n))
#define CV_8SC1 CV_MAKETYPE(CV_8S,1)
#define CV_8SC2 CV_MAKETYPE(CV_8S,2)
#define CV_8SC3 CV_MAKETYPE(CV_8S,3)
#define CV_8SC4 CV_MAKETYPE(CV_8S,4)
#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n))
#define CV_16UC1 CV_MAKETYPE(CV_16U,1)
#define CV_16UC2 CV_MAKETYPE(CV_16U,2)
#define CV_16UC3 CV_MAKETYPE(CV_16U,3)
#define CV_16UC4 CV_MAKETYPE(CV_16U,4)
#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n))
#define CV_16SC1 CV_MAKETYPE(CV_16S,1)
#define CV_16SC2 CV_MAKETYPE(CV_16S,2)
#define CV_16SC3 CV_MAKETYPE(CV_16S,3)
#define CV_16SC4 CV_MAKETYPE(CV_16S,4)
#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n))
#define CV_32SC1 CV_MAKETYPE(CV_32S,1)
#define CV_32SC2 CV_MAKETYPE(CV_32S,2)
#define CV_32SC3 CV_MAKETYPE(CV_32S,3)
#define CV_32SC4 CV_MAKETYPE(CV_32S,4)
#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n))
#define CV_32FC1 CV_MAKETYPE(CV_32F,1)
#define CV_32FC2 CV_MAKETYPE(CV_32F,2)
#define CV_32FC3 CV_MAKETYPE(CV_32F,3)
#define CV_32FC4 CV_MAKETYPE(CV_32F,4)
#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n))
#define CV_64FC1 CV_MAKETYPE(CV_64F,1)
#define CV_64FC2 CV_MAKETYPE(CV_64F,2)
#define CV_64FC3 CV_MAKETYPE(CV_64F,3)
#define CV_64FC4 CV_MAKETYPE(CV_64F,4)
#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n))
#define CV_16FC1 CV_MAKETYPE(CV_16F,1)
#define CV_16FC2 CV_MAKETYPE(CV_16F,2)
#define CV_16FC3 CV_MAKETYPE(CV_16F,3)
#define CV_16FC4 CV_MAKETYPE(CV_16F,4)
#define CV_16FC(n) CV_MAKETYPE(CV_16F,(n))
//! @}
//! @name Comparison operation
//! @sa cv::CmpTypes
//! @{
#define CV_HAL_CMP_EQ 0
#define CV_HAL_CMP_GT 1
#define CV_HAL_CMP_GE 2
#define CV_HAL_CMP_LT 3
#define CV_HAL_CMP_LE 4
#define CV_HAL_CMP_NE 5
//! @}
//! @name Border processing modes
//! @sa cv::BorderTypes
//! @{
#define CV_HAL_BORDER_CONSTANT 0
#define CV_HAL_BORDER_REPLICATE 1
#define CV_HAL_BORDER_REFLECT 2
#define CV_HAL_BORDER_WRAP 3
#define CV_HAL_BORDER_REFLECT_101 4
#define CV_HAL_BORDER_TRANSPARENT 5
#define CV_HAL_BORDER_ISOLATED 16
//! @}
//! @name DFT flags
//! @{
#define CV_HAL_DFT_INVERSE 1
#define CV_HAL_DFT_SCALE 2
#define CV_HAL_DFT_ROWS 4
#define CV_HAL_DFT_COMPLEX_OUTPUT 16
#define CV_HAL_DFT_REAL_OUTPUT 32
#define CV_HAL_DFT_TWO_STAGE 64
#define CV_HAL_DFT_STAGE_COLS 128
#define CV_HAL_DFT_IS_CONTINUOUS 512
#define CV_HAL_DFT_IS_INPLACE 1024
//! @}
//! @name SVD flags
//! @{
#define CV_HAL_SVD_NO_UV 1
#define CV_HAL_SVD_SHORT_UV 2
#define CV_HAL_SVD_MODIFY_A 4
#define CV_HAL_SVD_FULL_UV 8
//! @}
//! @name Gemm flags
//! @{
#define CV_HAL_GEMM_1_T 1
#define CV_HAL_GEMM_2_T 2
#define CV_HAL_GEMM_3_T 4
//! @}
//! @}
#endif 创建 Mat 对象
- 通过读入一张图像，直接转换为Mat对象
- 使用无参数构造函数，创建Mat对象
- 使用行、列、类型带这个三个参数的构造函数创建Mat对象
- 使用行、列、类型、Scalar向量四个参数的构造函数创建Mat对象
- 使用大小、类型两个参数的构造函数创建Mat对象
- 使用大小、类型、Scalar向量三个参数的构造函数创建Mat对象
- 使用Mat::zeros函数实现，两个参数一个是Size表示图像宽与高另外一个表示类型
- 使用Mat::ones函数实现，两个参数一个是Size表示图像宽与高，另外一个表示类型
// 创建mat对象
Mat t1 = Mat(256, 256, CV_8UC3);
// Scalar(...) 是一个标量，表示一个颜色值
// 灰度图像是一个通道的，一个参数就可以
t1 = Scalar(0, 0, 255);
imshow("t1", t1);
// 方法2
Mat t2 = Mat(Size(512, 512), CV_8UC3);
t2 = Scalar(255, 0, 255);
imshow("t2", t2);
// 创建时指定大小
Mat t3 = Mat::zeros(Size(256, 256), CV_8UC3);
imshow("t3", t3);
// 从现有的图像create
// 只是对象引用的赋值，本质上还是一张图像
// 如果对原图像进行修改，t4也会被修改
Mat t4 = src;
// 克隆，是一个新的对象
// 对原图进行修改，t5并不改变
Mat t5 = src.clone();
imshow("t5", t5);
// 拷贝
Mat t6;
src.copyTo(t6);
imshow("t6", t6);
// 创建一个和现有图像大小、类型一样的空白图像
Mat t7 = Mat::zeros(src.size(), src.type());Mat 像素信息遍历
/*
for (int row = 0; row < height; row++) {
for (int col = 0; col < width; col++) {
if (dim == 3) {
Vec3b pixel = src.at(row, col);
int blue = pixel[0];
int green = pixel[1];
int red = pixel[2];
src.at(row, col)[0] = 255 - blue;
src.at(row, col)[1] = 255 - green;
src.at(row, col)[2] = 255 - red;
}
if (dim == 1) {
int pv = src.at(row, col);
src.at(row, col) = (255 - pv);
}
}
}
imshow("fan", src);
*/
// 指针方式遍历，并拷贝图像
Mat result = Mat::zeros(src.size(), src.type());
for (int row = 0; row < height; row++) {
uchar* curr_row = src.ptr(row);
uchar* result_row = result.ptr(row);
for (int col = 0; col < width; col++) {
if (dim == 3) {
int blue = *curr_row++;
int green = *curr_row++;
int red = *curr_row++;
*result_row++ = blue;
*result_row++ = green;
*result_row++ = red;
}
if (dim == 1) {
int pv = *curr_row++;
*result_row++ = pv;
}
}
}
imshow("src", src); Mat对象起源
当OpenCV 1.0发布时候没有Mat对象，是个C语言风格的数据结构IPlImage来表示内存中图像对象，但是OpenCV开发者在做复杂图像处理算法分析与计算时候，创建了很多IplImage这样的数据结构，偶尔最后可能忘记释放内存了，这样算法就有了内存泄漏，导致开发者浪费很多精力去寻找这个错误，这个已经跟图像处理算法没有关系了，但是它却困扰了很多OpenCV开发者。Intel发现自己做的库居然这么渣，广大群众不满意，决定从OpenCV 2.0开始使用一个新的内存对象Mat来表示内存中的图像对象。它是C++风格的数据结构，自动实现内存分配与回收，这样OpenCV开发者就再也不用担心因为使用OpenCV的图像对象数据结构而导致内存泄漏问题了。但是仍然有些开发者直到现在还在顽固的使用IplImage这个对象，Intel为了照顾他们，所以允许IplImage对象继续存在，还提供可以把IplImage对象转换成Mat对象的构造函数，作为开源SDK可谓仁至义尽。