这篇文章整理了常用opencv函数(用途、参数的意义，必要时也会列出该函数所在的头文件)以及一些其它关于opencv库的知识。补充了一些版本之间的头文件、函数等的调整。

一、基础

1

  • 查看版本
pkg-config --modversion opencv

如果要看cv 头文件里面具体的cpp实现，必须要去源码里面看。安装opencv的时候那些具体实现都被编译成二进制的库了。只能跳转到头文件。

3. 版本改动

OpenCV2到OpenCV3之间的改动比较小，而且现在的这些版本已经比较老了。所以此处给出的改动主要是2和3改到OpenCV4。可以参考此处，给出了很多有用的信息。

1. 头文件变动
/* old version ---> compatible with ros-noetic*/
#include <opencv/cv.h> //---> <opencv2/opencv.hpp>
#include <opencv/cxcore.h> //---> <opencv2/core/core_c.h>
#include <opencv/highgui.h> //---> <opencv2/highgui.hpp>
#include <opencv/imgproc.hpp> //---> <opencv2/imgproc.hpp>
2. was not declared in this scope

如果使用的opencv为3.2.0。

// CV_WINDOW_AUTOSIZE’ was not declared in this scope
#include <opencv2/highgui/highgui_c.h>

// cvGetWindowHandle’ was not declared in this scope
#include <opencv2/highgui/highgui_c.h>

// CV_LOAD_IMAGE_UNCHANGED’ was not declared in this scope
#include <opencv2/imgcodecs/imgcodecs_c.h>
3. 判断所用的opencv的版本
#include <iostream>
#include <opencv2/core/version.hpp>
#include <opencv2/core/core.hpp>
 
using namespace cv;
 
#if CV_VERSION_EPOCH == 2
#define OPENCV2
#elif CV_VERSION_MAJOR == 3
#define  OPENCV3
#else
#error Not support this OpenCV version

5

cvMat停用,只能用cv::Mat. cvCreateMat是老版本产生cvMat的函数，应改用新的数据结构和构造函数。

二、

1. data()

2

三、matrix

  • cvSVD calculate singular values and singular vectors. flag is below. pic1
// (输入矩阵,结果奇异值矩阵,可选的左部正交矩阵,可选右部正交矩阵,flag): 奇异值都是非负的并按降序存储
void cvSVD( CvArr* A, CvArr* W, CvArr* U = NULL, CvArr* V = NULL, int flags = 0);

3. solve

// src mat: square size and be symmetrical
// the eigenvalues are stored in the descending order. the eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues.
// https://docs.opencv.org/3.4/d2/de8/group__core__array.html#ga9fa0d58657f60eaa6c71f6fbb40456e3
bool cv::eigen (InputArray src, OutputArray eigenvalues, OutputArray eigenvectors = noArray() );

// ( left-hand input matrix, right-hand input matrix, output solution, matrix decomposition types)
bool solve(const Mat & src1, const Mat & src2, Mat & dst, int flags = DECOMP_LU); 

//matVA is eigenvalues in the descending order 降序保存的特征值; matVE is eigenvector 特征向量
cv::eigen(matAC, matVA, matVE);

OpenCV allows the distortion coefficients vector to have several possible lengths:

Number of values Meaning
4 [k1, k2, p1, p2] — the most common case (two radial, two tangential)
5 [k1, k2, p1, p2, k3] — adds a 3rd radial term
8 or 12 [k1, k2, p1, p2, k3, k4, k5, k6, s1, s2, s3, s4] — for advanced camera models (rational or thin-prism) 
// (in,out,*,*,new camera matrix)
cv::undistortPoints(ptc1, ptc2, cameraMatrix, distCoeffs, cameraMatrix);

camera

undistortion

new camera intrinsic matrix for fisheye camera

  • K: Original intrinsic matrix
  • D: Distortion coefficients from cv::fisheye::calibrate
  • imageSize: Image size
  • balance: 0 or 1, tradeoff between “keeping field of view” vs. “removing black borders”
  • K_new: Output “optimal” matrix for undistortion
#include <opencv2/calib3d.hpp>

cv::Mat K_new;
cv::fisheye::estimateNewCameraMatrixForUndistortRectify(
    K, D, imageSize, cv::Matx33d::eye(), K_new, balance, imageSize, 1.0);

cv::Mat K, D; // original intrinsics and distortion (fisheye)
cv::fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, image_size, cv::Matx33d::eye(), K_new);
cv::Mat map1, map2;

cv::fisheye::initUndistortRectifyMap(K, D, cv::Matx33d::eye(), K_new, image_size, CV_16SC2, map1, map2);

Then:

  • K_new is the intrinsic matrix of the undistorted image
  • Distortion coefficients are [0, 0, 0, 0] (the undistorted image is rectified and has no distortion)

new camera intrinsic matrix for pinhole camera

cv::Mat new_K = cv::getOptimalNewCameraMatrix(K, distort_coeffs, image_size, alpha, new_image_size);
cv::Mat map1, map2;
cv::initUndistortRectifyMap(K, distort_coeffs, cv::Mat(), new_K, new_image_size, CV_16SC2, map1, map2);

After this:

  • Intrinsic: new_K
  • Distortion: [0, 0, 0, 0, 0]

pnp

cv::solvePnP assumes the pinhole camera model with radial/tangential distortion, not a fisheye projection. The result of cv::solvePnP gives the camera pose with respect to the world frame expressed in the camera frame. The returned translation vector describing the transformation from the world coordinate system to the camera coordinate system.

other

colorize

applyColorMap
void cv::applyColorMap(InputArray src, OutputArray dst, ColormapTypes colormap);

Input must be CV_8UC1 (grayscale). If not, convert using:

cv::Mat gray;
cv::normalize(src, gray, 0, 255, cv::NORM_MINMAX);
gray.convertTo(gray, CV_8U);

Common Colormaps

Name Constant Description
COLORMAP_JET 2 Blue → Green → Red (like MATLAB “jet”)
COLORMAP_HOT 11 Black → Red → Yellow → White
COLORMAP_COOL 8 Cyan → Magenta
COLORMAP_VIRIDIS 16 Smooth perceptually uniform map (modern default)
COLORMAP_INFERNO 17 High-contrast “fire-like” map
COLORMAP_TURBO 22 Google’s “turbo” perceptual map (vivid, balanced)
COLORMAP_PARULA 12 MATLAB’s “parula”
COLORMAP_MAGMA 18 Black → Red → White gradient
COLORMAP_CIVIDIS 21 Color-blind friendly variant of “viridis”

LUT(Look-Up Table)

cv::LUT() (Look-Up Table) replaces each pixel’s intensity value (0–255) in a grayscale image with a corresponding color from a user-defined table.

Function Signature

void cv::LUT(InputArray src, InputArray lut, OutputArray dst);
// src: single-channel grayscale image (CV_8UC1)
// lut: lookup table (size 256×1, type CV_8UC3)
// dst: output color image (CV_8UC3)

Eample

#include <opencv2/opencv.hpp>
#include <iostream>

int main() {
    // Step 1. Create a grayscale test image
    cv::Mat gray(256, 256, CV_8UC1);
    for (int i = 0; i < 256; ++i)
        gray.row(i).setTo(i);

    // Step 2. Create custom LUT (Blue → White → Red)
    cv::Mat lut(1, 256, CV_8UC3);

    for (int i = 0; i < 256; ++i) {
        cv::Vec3b color;
        if (i < 128) {
            // Blue → White
            float ratio = i / 127.0f;
            color[0] = 255;                       // Blue stays high
            color[1] = static_cast<uchar>(255 * ratio); // Green increases
            color[2] = static_cast<uchar>(255 * ratio); // Red increases
        } else {
            // White → Red
            float ratio = (i - 128) / 127.0f;
            color[0] = static_cast<uchar>(255 * (1.0f - ratio)); // Blue decreases
            color[1] = static_cast<uchar>(255 * (1.0f - ratio)); // Green decreases
            color[2] = 255;                       // Red stays high
        }
        lut.at<cv::Vec3b>(0, i) = color;
    }

    // Step 3. Apply the LUT
    cv::Mat colored;
    cv::LUT(gray, lut, colored);

    // Step 4. Show results
    cv::imshow("Gray", gray);
    cv::imshow("Custom ColorMap (Blue-White-Red)", colored);
    cv::waitKey(0);
    return 0;
}