C++ OpenCV模擬實現微信跳一跳

//最大執行次數
#define MaxRound 100

#pragma once
#include <opencv2/opencv.hpp>
#include <iostream>
#include <opencv.hpp>
#include <windows.h>

//跳一跳作弊程序
//版本 v1.0.2 作者：CSDN陳千裡

/*
* 程序使用說明：
* 需要配合mumu模擬器使用，電腦需要安裝adb調試工具，和opencv庫。
* 程序原理介紹:
* 通過計算兩點之間的距離，估算跳躍的長度，按壓屏幕的時間間隔
*
* 參考論文：
* 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使用：
* https://blog.csdn.net/u010042669/article/details/104066744
* Canny 邊緣檢測：
* https://blog.csdn.net/hensonwells/article/details/112557073
*/


#include "pch.h"
#include <windows.h>
#include <sstream>
using namespace cv;

Mat srcImage;//存放跳一跳的截圖
Mat blackPeopleTem;//黑色小人匹配圖
std::stringstream ssm; //int轉string
//最大執行次數
#define MaxRound 100

//由於分辨率的不同，微調終點的位置
#define Tuning 0.52f

//Debug函數
void DebugImg(const std::string& fileName, Mat& mat, const Point& point);
void DebugImg(const std::string& fileName, Mat& mat);
//刷新srcImage的信息（截圖）
void refreshSrcImage() {
	system("adb shell screencap -p /sdcard/ScreenCatch.png");
	//您需要自定義的地方，下面的"C:\\adb"
	system("adb pull /sdcard/ScreenCatch.png C:\\adb\\temp");
	srcImage = imread("C:\\adb\\temp\\ScreenCatch.png");
}

//尋找跳一跳黑色小人的位置
Point GetNowPoint(Mat& srcImage, Mat& Tem_img) {
	cv::Mat image_matched;
	matchTemplate(srcImage, Tem_img, image_matched, TM_CCORR_NORMED);// 匹配黑棋子
	double minVal, maxVal;
	Point minLoc, maxLoc, matchLoc;
	DebugImg("黑人匹配圖.png", image_matched);
	minMaxLoc(image_matched, &minVal, &maxVal, &minLoc, &maxLoc, Mat());
	matchLoc = maxLoc; //matchLoc是最佳匹配的區域左上角點
	//調試輸出
	DebugImg("1黑人位置.png", srcImage, Point(matchLoc.x + Tem_img.cols, matchLoc.y + Tem_img.rows));
	//DebugImg("1黑人位置.png", srcImage, Point(matchLoc.x + Tem_img.cols * 0.5, matchLoc.y + Tem_img.rows));
	return Point(matchLoc.x, matchLoc.y);
}

//獲得小方塊的目標點
Point GetNextPoint(Mat& srcImage) {
	cv::Point point1;
	cv::Point point2;
	cv::GaussianBlur(srcImage, srcImage, cv::Size(5, 5), 0);  //高斯濾波，降低噪聲
	Mat temp, temp2;
	//cv::threshold(srcImage, temp, 0, 255, 8);
	//srcImage = temp;
	Canny(srcImage, temp, 20, 30);      //進行邊緣檢測
	temp2 = srcImage;
	srcImage = temp;
	std::vector<std::vector<Point>> contours;
	std::vector<Vec4i> hierarchy;
	findContours(srcImage, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point()); //找到關鍵的角點
	//遍歷每一個輪廓，把多餘的輪廓去掉
	std::vector<std::vector<cv::Point> >::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個輪廓，contours[i].size()代表的是第i個輪廓上所有的像素點數  
		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];    //記錄  y值最低點坐標
				nIdY = i;                   //記錄哪個區域內的
			}
		}
	}
	int minY = srcImage.cols;
	for (int j = 0; j < contours[nIdY].size(); j++) { //在哪個區域內繼續變量 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;     //記錄X點的最大值
		}
	}
	//調試輸出
	DebugImg("2目標點位置.png", temp2, Point(point1.x, point2.y));
	DebugImg("邊緣圖.png", srcImage, Point(point1.x, point2.y));
	return cv::Point(point1.x, point2.y);       //返回中點坐標
}


//計算兩個點的距離
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 > 600) {
		std::cout << "距離探測失誤" << std::endl;
		result = 230;
	}
	return result;
}

//模擬按壓屏幕跳躍
void Jump(float& g_distance) {
	std::cout << "distance:" << g_distance << std::endl;
	int time = std::ceil(g_distance * 4 * Tuning);
	std::string str_Time, str;
	//模擬長按屏幕
	ssm.clear();
	ssm << time;
	ssm >> str_Time;
	str = "adb shell input swipe 461 1203 461 1203 " + str_Time;
	std::cout << str << std::endl;
	system(str.c_str());
}

//主過程
void Process() {
	Point pBlackPeople;
	Point pFinish;
	float dis;
	for (int i = 0; i < MaxRound; i++) {
		refreshSrcImage();
		pBlackPeople = GetNowPoint(srcImage, blackPeopleTem);
		pFinish = GetNextPoint(srcImage);
		dis = GetDistance(pBlackPeople, pFinish);
		Jump(dis);
		Sleep(2000);
	}
}

int main() {
	/*srcImage = imread("C:/adb/Test/1.png");
	blackPeopleTem = imread("C:/adb/Resources/character3.png");
	GetNowPoint(srcImage, blackPeopleTem);*/

	//首先要鏈接端口
	system("adb connect 127.0.0.1:7555");
	refreshSrcImage();
	blackPeopleTem = imread("C:/adb/Resources/character3.png");
	//初始化到此結束
	Process();

	int x = 280; // 裁剪區域起始點 x坐標
	int y = 400; // 裁剪區域起始點 y坐標
	int width = 100; // 裁剪區域寬度
	int height = 100; // 裁剪區域高度

	//Rect area(x, y, width, height);
	//Mat guide_roi = srcImage(Rect(x, y, width, height));


	//測試代碼
	//namedWindow("test opencv setup", WINDOW_AUTOSIZE);
	//imshow("test opencv setup", srcImage);
	//waitKey(0);
	return 0;
}

//保存圖片和畫點，用於調試
void DebugImg(const std::string& fileName, Mat& mat, const Point& point) {
	Mat temp = mat;
	//在圖片上面畫點
	circle(temp, point, 5, Scalar(0, 0, 255), -1);
	std::string path = "c:/adb/temp/", sR;
	sR = path + fileName;
	imwrite(sR, temp);
}

void DebugImg(const std::string& fileName, Mat& mat) {
	std::string path = "c:/adb/temp/", sR;
	sR = path + fileName;
	imwrite(sR, mat);
}