data types in opencv and types conversion.

cv::Mat

declaration and initialization

//元素类型为float；Scalar::all(0)：所有元素赋值为0
cv::Mat matA(3, 3, CV_32F, cv::Scalar::all(0));

cvSetZero: for dense, set all element as 0; for sparse, remove all elements.

void cvSetZero( CvArr* arr ) ;

scalar

penCV matrix types follow the naming pattern:

CV_<bit_depth><S|U|F>C<number_of_channels>

bit_depth: number of bits per channel
S: signed integer
U: unsigned integer
F: floating point
C<number>: number of channels
CV_8U = 8-bit unsigned (0–255)
CV_8S = 8-bit signed (−128–127)
CV_16U = 16-bit unsigned
CV_16S = 16-bit signed
CV_32S = 32-bit signed integer
CV_32F = 32-bit floating point
CV_64F = 64-bit floating point

access

cvmSet and cvmGet is 访问CV_32FC1和CV_64FC1型数组的最简便的方式，其访问速度和直接访问几乎相同.

Scalar value;
// (out, in, in, in)
cvmSet( mat,row, col, value);
// (in,in,in)
value = cvmGet( mat, row, col);

//CV_[The number of bits per item][Signed or Unsigned][Type Prefix]C[The channel number]
//CV_8UC3：8位的 unsigned char 型，每个像素由三个元素组成三通道

cv::Mat的赋值=，默认是浅拷贝。

type conversion

conversion between mat and array

数组转为mat

unsigned char cbuf[height][width];
cv::Mat img(height, width, CV_8UC1, (unsigned char*)cbuf);

mat转为数组

unsigned char *array=new unsigned char[mat.rows*mat.cols];
 
if (mat.isContinuous())
    array = mat.data;

conversion between mat and std::vector

cv::Mat a,b;
std::vector<double> vec;

a.convertTo(b,CV_64F);
vec = std::vector<double>(b.reshape(1,b.rows*b.cols)); // 把b转换为一个行矩阵，再转换为向量

a = cv::Mat(vec,true);

conversion between mat with different types

cv::Mat mat_23f(2, 3, CV_32F);
mat_23f = (cv::Mat_<float>(2, 3) << 
 1, 2, 3, 
 4, 5, 6);
cv::Mat mat_23d(2, 3, CV_64F);
mat_23f.convertTo(mat_23d, CV_64F);

IplImage

IplImage image2;
cv_bridge::CvImagePtr cv_ptr;
// initialize cv_ptr
image2.imageData = (char *) cv_ptr->image.data;

详细参考此处

cv::Vec3x

cv::Vec3b is OpenCV’s small, fixed-size vector type with 3 elements of type uchar (unsigned char, 0–255).
cv::Vec3f: float
cv::Vec3d: double
cv::Vec3i: int

color conversion

CIELAB

The CIE Lab (often written as CIELAB) color space is a perceptually uniform color model designed to approximate how human vision perceives color differences. Equal numerical differences correspond roughly to equal perceived color differences.

Channel	Meaning	Typical Range (OpenCV float)	Interpretation
L*	Lightness	0 → 100	0 = black, 100 = white
a*	Green–Red	−128 → +127	Negative = green, Positive = red
b*	Blue–Yellow	−128 → +127	Negative = blue, Positive = yellow

conversion between color and lab

cv::cvtColor(bgr, lab, cv::COLOR_BGR2Lab);
cv::cvtColor(lab, bgr, cv::COLOR_Lab2BGR);

converts an image from the BGR color space (the default used by OpenCV when reading images) to the CIE Lab color space.

practical usage

Segmentation: you can threshold only the a and b channels to separate colors regardless of brightness.
Color correction: modify only L for lightness, leaving color intact.
Difference measurement: Use the ΔE metric (e.g., ΔE₀₀) to measure how similar two colors are.

opencv-types