解决高版本opencv4.3 关于基本数据类型cvPoint、CvScalar的报错

本文主要是介绍解决高版本opencv4.3 关于基本数据类型cvPoint、CvScalar的报错，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

未定义标识符cvPoint！！！！CvScalar，找了半天估计是版本不兼容，换了包含的头文件了，找了好多都是好几年前的代码。

如图，或者是直接#include "opencv.h"的都还有..

作为一个今天才配好VS2019+Opencv4.3+Opencv-Contrib+Cmake 的人来说测试段代码太难了！

言归正传，可以看看这个官网的文档（不是别的地方的，比如这个w3cschool的也有点问题）

链接：https://www.w3cschool.cn/search?w=CvPoint

然后那个地方的代码也是运行不了的，也报这个cvpoint未定义，如下：

#include <stdio.h>
#include <iostream>
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/xfeatures2d.hpp"
using namespace cv;
using namespace cv::xfeatures2d;
void readme();
/* @function main */
int main( int argc, char** argv )
{if( argc != 3 ){ readme(); return -1; }Mat img_object = imread( argv[1], IMREAD_GRAYSCALE );Mat img_scene = imread( argv[2], IMREAD_GRAYSCALE );if( !img_object.data || !img_scene.data ){ std::cout<< " --(!) Error reading images " << std::endl; return -1; }//-- Step 1: Detect the keypoints and extract descriptors using SURFint minHessian = 400;Ptr<SURF> detector = SURF::create( minHessian );std::vector<KeyPoint> keypoints_object, keypoints_scene;Mat descriptors_object, descriptors_scene;detector->detectAndCompute( img_object, Mat(), keypoints_object, descriptors_object );detector->detectAndCompute( img_scene, Mat(), keypoints_scene, descriptors_scene );//-- Step 2: Matching descriptor vectors using FLANN matcherFlannBasedMatcher matcher;std::vector< DMatch > matches;matcher.match( descriptors_object, descriptors_scene, matches );double max_dist = 0; double min_dist = 100;//-- Quick calculation of max and min distances between keypointsfor( int i = 0; i < descriptors_object.rows; i++ ){ double dist = matches[i].distance;if( dist < min_dist ) min_dist = dist;if( dist > max_dist ) max_dist = dist;}printf("-- Max dist : %f \n", max_dist );printf("-- Min dist : %f \n", min_dist );//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )std::vector< DMatch > good_matches;for( int i = 0; i < descriptors_object.rows; i++ ){ if( matches[i].distance <= 3*min_dist ){ good_matches.push_back( matches[i]); }}Mat img_matches;drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );//-- Localize the objectstd::vector<Point2f> obj;std::vector<Point2f> scene;for( size_t i = 0; i < good_matches.size(); i++ ){//-- Get the keypoints from the good matchesobj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );}Mat H = findHomography( obj, scene, RANSAC );//-- Get the corners from the image_1 ( the object to be "detected" )std::vector<Point2f> obj_corners(4);obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );std::vector<Point2f> scene_corners(4);perspectiveTransform( obj_corners, scene_corners, H);//-- Draw lines between the corners (the mapped object in the scene - image_2 )line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );//-- Show detected matchesimshow( "Good Matches & Object detection", img_matches );waitKey(0);return 0;}/* @function readme */void readme(){ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }

上官方文档找到了这个

链接：https://docs.opencv.org/3.2.0/dc/dd1/structCvPoint.html