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Editor to share with you how C++ OpenCV simulates the realization of Wechat jump. I hope you will get something after reading this article. Let's discuss it together.

Real machine demonstration Gif:

Train of thought:

Get the position of the little black man, get the position of the target box, calculate the distance between the two, and then calculate the time of rough pressing on the screen.

Specific implementation 1: use mumu simulator to get screenshots

Use the mumu simulator to simulate the mobile phone, then use the adb debugging tool to take screenshots, save them locally, and then get the local screenshots from the OpenCV program.

Concrete realization 2: use adb tool to simulate pressing

After calculating the distance and time, consider using the method of simulated pressing the screen to control the movement of the villain.

Specific implementation 3: the position of the press is just on the "do it again" button.

In this way, even if the jump fails, Mini Program will keep jumping as long as the user does not stop.

Get the location of the little black man:

It's simple, just use OpenCV's matchTemplate, and pay attention to using the "TM_CCORR_NORMED" method.

Get the location of the end point:

Canny edge detection algorithm is used here.

Need to be customized:

A folder that saves pictures from the mumu simulator to a local directory folder. And Debug's cache directory.

You can also customize the number of cycles the program runs:

/ / maximum number of execution # define MaxRound 100

Just modify the following 100.

And your matching pattern picture location:

Character3.png

Complete project:

Project configuration: DebugX64, including header file opencv header file, lib selected as opencv_world425d.dll (seems to be the name), this lib must have d, because we are Debug mode, so use this library. Then the additional input of the linker is also filled in this option.

Project dependencies: adb, opencv425

The following is the complete project reference.

Project structure

Pch.h#pragma once#include # include main.cpp// hop cheating program / / version v1.0.2 author: CSDN Chen Qianli / * * Program instructions: * need to be used with mumu simulator, the computer needs to install adb debugging tools, and opencv library. * introduction to the principle of the program: * estimate the length of the jump by calculating the distance between two points Time interval between pressing the screen * * reference paper: * https://blog.csdn.net/qq_37406130/article/details/79007335* https://blog.csdn.net/sundy_2004/article/details/7749093* https://blog.csdn.net/q5222890/article/details/105533233* https://blog.csdn.net/qq_47342178/article/details/109779840* adb swip use: * https://blog.csdn.net/u010042669/article/details/104066744* Canny edge detection: * https://blog.csdn.net/hensonwells/article/details/112557073*/#include "pch.h" # include # include using namespace cv Mat srcImage;// stores one-hop screenshots of Mat blackPeopleTem;// black villain matching image std::stringstream ssm; / / int to string// maximum number of execution # define MaxRound 100amp / due to different resolution, fine-tune the location of the end point # define Tuning 0.52f//Debug function void DebugImg (const std::string& fileName, Mat& mat, const Point& point); void DebugImg (const std::string& fileName, Mat& mat) / / refresh srcImage information (screenshot) void refreshSrcImage () {system ("adb shell screencap-p / sdcard/ScreenCatch.png"); / / where you need to customize, the following "C:\\ adb" system ("adb pull / sdcard/ScreenCatch.png C:\\ adb\\ temp"); srcImage = imread ("C:\ adb\\ temp\\ ScreenCatch.png") } / / looking for the location of the black villain Point GetNowPoint (Mat& srcImage, Mat& Tem_img) {cv::Mat image_matched; matchTemplate (srcImage, Tem_img, image_matched, TM_CCORR_NORMED); / / matching black chess pieces double minVal, maxVal; Point minLoc, maxLoc, matchLoc; DebugImg ("black matching figure .png", image_matched) MinMaxLoc (image_matched, & minVal, & maxVal, & minLoc, & maxLoc, Mat ()); matchLoc = maxLoc; / / matchLoc is the best match in the upper left corner of the area / / debug output DebugImg ("1 black position .png", srcImage, Point (matchLoc.x + Tem_img.cols, matchLoc.y + Tem_img.rows)) / / DebugImg ("1 black position .png", srcImage, Point (matchLoc.x + Tem_img.cols * 0.5, matchLoc.y + Tem_img.rows)); return Point (matchLoc.x, matchLoc.y);} / / get the target point of the cube Point GetNextPoint (Mat& srcImage) {cv::Point point1; cv::Point point2; cv::GaussianBlur (srcImage, srcImage, cv::Size (5,5), 0) / / Gaussian filtering to reduce noise Mat temp, temp2; / / cv::threshold (srcImage, temp, 0,255,8); / / srcImage = temp; Canny (srcImage, temp, 20,30); / / Edge detection temp2 = srcImage; srcImage = temp; std::vector contours; std::vector hierarchy FindContours (srcImage, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point ()); / / find key corners / / traverse each outline and remove the extra contours std::vector::const_iterator it = contours.begin (); while (it! = contours.end ()) {if (it- > size ())

< 150) it = contours.erase(it); else ++it; } int nYMin = srcImage.rows; int nXMin = srcImage.cols; int nYMax = 0; int nXMax = 0; int nIdY = 0; for (int i = 0; i < contours.size(); i++) { //contours[i]代表的是第i个轮廓，contours[i].size()代表的是第i个轮廓上所有的像素点数 for (int j = 0; j < contours[i].size(); j++) { if (contours[i][j].y < nYMin) { nYMin = contours[i][j].y; //找到最低的y值 point1 = contours[i][j]; //记录 y值最低点坐标 nIdY = i; //记录哪个区域内的 } } } int minY = srcImage.cols; for (int j = 0; j < contours[nIdY].size(); j++) { //在哪个区域内继续变量 找到x最大值 if (contours[nIdY][j].x >

NXMax) {nXMax = contours [nIdY] [j] .x;}} for (int j = 0; j)

< contours[nIdY].size(); j++) {//找到x中最大值上的最小值 if (contours[nIdY][j].x == nXMax && contours[nIdY][j].y < minY) { point2 = contours[nIdY][j]; minY = contours[nIdY][j].y; //记录X点的最大值 } } //调试输出 DebugImg("2目标点位置.png", temp2, Point(point1.x, point2.y)); DebugImg("边缘图.png", srcImage, Point(point1.x, point2.y)); return cv::Point(point1.x, point2.y); //返回中点坐标}//计算两个点的距离float GetDistance(Point& first_point, Point& next_point) { float A = first_point.x - next_point.x; float B = first_point.y - (next_point.y + 50); float result = pow(pow(A, 2) + pow(B, 2), 0.5); if (result >

{std::cout

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