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使用OpenCV模板匹配进行Qt桌面仪表数字识别（详细攻略）

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前言

导师让干的活，要传给别人，记录一下自己的付出和成果

一、开发目的

        由于某些企业的仪表盘界面，还需进行人工读取和记录，所以要使用程序来解放人民的双手。且由于电脑系统还是上古时代的产品，所以代码占用内存要非常小。

二、项目框架

        1.仪表制作： 此小项目需要用qt制作界面，模拟某企业的仪表盘进行识别。

        2.识别输入：采用微软提供的一组C++模板库来对qt界面进行截图，并作为识别的输入。

        3.识别：采用opencv模板匹配进行识别，模板匹配简单易用，但对于旋转、缩放、光照变化等情况的鲁棒性较差。对于这些复杂情况，可能需要结合其他更高级的图像处理技术，如轮廓检测或深度学习等方法。

三、qt界面制作

 ui->setupUi(this);
    //this->resize(1440,900);
    ui->lcdNumber_1->setDigitCount(4);
    ui->lcdNumber_1->setStyleSheet("background:black;color:#4dff00;");

    ui->lcdNumber_2->setDigitCount(4);
    ui->lcdNumber_2->setStyleSheet("background:black;color:#4dff00;");

    ui->lcdNumber_3->setDigitCount(4);
    ui->lcdNumber_3->setStyleSheet("background:black;color:#4dff00;");

    ui->lcdNumber_4->setDigitCount(4);
    ui->lcdNumber_4->setStyleSheet("background:black;color:#4dff00;");

    ui->lcdNumber_5->setDigitCount(4);
    ui->lcdNumber_5->setStyleSheet("background:black;color:#ff0022;");

    ui->lcdNumber_6->setDigitCount(4);
    ui->lcdNumber_6->setStyleSheet("background:black;color:#ff0022;");

    ui->lcdNumber_7->setDigitCount(4);
    ui->lcdNumber_7->setStyleSheet("background:black;color:#ff0022;");

    ui->lcdNumber_8->setDigitCount(4);
    ui->lcdNumber_8->setStyleSheet("background:black;color:#ff0022;");

    ui->lcdNumber_9->setDigitCount(4);
    ui->lcdNumber_9->setStyleSheet("background:black;color:#4dff00;");

    ui->lcdNumber_10->setDigitCount(4);
    ui->lcdNumber_10->setStyleSheet("background:black;color:#4dff00;");

    ui->lcdNumber_11->setDigitCount(4);
    ui->lcdNumber_11->setStyleSheet("background:black;color:#4dff00;");

    ui->lcdNumber_12->setDigitCount(4);
    ui->lcdNumber_12->setStyleSheet("background:black;color:#4dff00;");


    float num1,num2,num3,num4,num5,num6,num7,num8,num9,num10,num11,num12;
    num1 = (qrand()%(40-10)+10)*0.4;
    num2 = (qrand()%(40-10)+10)*0.4;
    num3 = (qrand()%(40-10)+10)*0.4;
    num4 = (qrand()%(40-10)+10)*0.4;
    num5 = (qrand()%(40-10)+10)*0.4;
    num6 = (qrand()%(40-10)+10)*0.4;
    num7 = (qrand()%(40-10)+10)*0.4;
    num8 = (qrand()%(40-10)+10)*0.4;
    num9 = (qrand()%(40-10)+10)*0.4;
    num10 = (qrand()%(40-10)+10)*0.4;
    num11 = (qrand()%(40-10)+10)*0.4;
    num12 = (qrand()%(40-10)+10)*0.4;

    num1 = (qrand()%(40-10)+10)*0.4;
    num2 = (qrand()%(40-10)+10)*0.4;
    num3 = (qrand()%(40-10)+10)*0.4;
    num4 = (qrand()%(40-10)+10)*0.4;
    num5 = (qrand()%(40-10)+10)*0.4;
    num6 = (qrand()%(40-10)+10)*0.4;
    num7 = (qrand()%(40-10)+10)*0.4;
    num8 = (qrand()%(40-10)+10)*0.4;
    num9 = (qrand()%(40-10)+10)*0.4;
    num10 = (qrand()%(40-10)+10)*0.4;
    num11 = (qrand()%(40-10)+10)*0.4;
    num12 = (qrand()%(40-10)+10)*0.4;

    ui->lcdNumber_1->display(num1);
    ui->lcdNumber_2->display(num2);
    ui->lcdNumber_3->display(num3);
    ui->lcdNumber_4->display(num4);
    ui->lcdNumber_5->display(num5);
    ui->lcdNumber_6->display(num6);
    ui->lcdNumber_7->display(num7);
    ui->lcdNumber_8->display(num8);
    ui->lcdNumber_9->display(num9);
    ui->lcdNumber_10->display(num10);
    ui->lcdNumber_11->display(num11);
    ui->lcdNumber_12->display(num12);
        界面制作比较简单，只需拉几个仪表图再用函数显示随机数，并用定时器设置固定时间变换一次就ok。

四、截取界面

bool SavePic(wstring name, HWND hWnd) {
	HDC hDc = NULL;
	hWnd = (hWnd == NULL) ? GetDesktopWindow() : hWnd;
	hDc = GetDC(hWnd); //获取DC
	if (hDc == NULL) return false;
	int bitOfPix = GetDeviceCaps(hDc, BITSPIXEL); //获取DC像素的大小
	int width = 1440;  //获取DC宽度
	int hight = 900;  //获取DC高度
	UINT dpi = GetDpiForWindow(hWnd); //获取dpi
	float fold; //根据dpi计算放大倍数
	switch (dpi) {
	case 96:
		fold = 1;
		break;
	case 120:
		fold = 1.25;
		break;
	case 144:
		fold = 1.5;
		break;
	case 192:
		fold = 2;
		break;
	case 216:
		fold = 2.25;
		break;
	default:
		fold = 1;
		break;
	}
	width *= fold; //复原宽度
	hight *= fold; //复原高度
	CImage image;
	image.Create(width, hight, bitOfPix); //为图像类创建与窗口DC相同大小的DC
	BitBlt(image.GetDC(), 0, 0, width, hight, hDc, 0, 0, SRCCOPY); //将窗口DC图像复制到image

	//name = to_wstring(i) + name;
	image.Save(name.data(), Gdiplus::ImageFormatPNG); //保存为png格式图片文件
	image.ReleaseDC(); //释放DC
	ReleaseDC(hWnd, hDc); //释放DC资源

}

五、模板匹配

vector<Mat> getcontour(Mat imgDil, Mat imgThre)//判断轮廓
{
	vector<vector<Point>> contour;
	vector<Vec4i> hierarchy;
	findContours(imgDil, contour, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_TC89_KCOS);
	vector<vector<Point>> conPoly(contour.size());
	vector<Rect> boundRect(contour.size());
	vector<Mat> imgCrop;
	for (int i = 0; i < contour.size(); i++)
	{
		int area = contourArea(contour[i]);//轮廓面积用于排除黑点
		cout << area << endl;
		string objectType;//形状
		if (area > 1000)//排除小黑圆点的干扰
		{
			float peri = arcLength(contour[i], true);
			approxPolyDP(contour[i], conPoly[i], 0.02 * peri, true);//把一个连续光滑曲线折线化			
			cout << conPoly[i].size() << endl;//边数
			boundRect[i] = boundingRect(conPoly[i]);

			int objCor = (int)conPoly[i].size();
			if (objCor == 3) { objectType = "Tri"; }
			else if (objCor == 4) {
				float aspRatio = (float)boundRect[i].width / boundRect[i].height;//长宽比来区别正方形与矩形
				cout << aspRatio << endl;
				if (aspRatio > 0.95 && aspRatio < 1.05) { objectType = "Square"; }
				else { objectType = "Rect"; }
			}
			else if (objCor > 4) { objectType = "Circle"; }
			drawContours(imgThre, conPoly, i, Scalar(255, 0, 255), 2);
			rectangle(imgThre, boundRect[i].tl(), boundRect[i].br(), Scalar(0.255, 0), 5);//框出图形
			putText(imgThre, objectType, { boundRect[i].x,boundRect[i].y - 5 }, 1, FONT_HERSHEY_PLAIN, Scalar(0, 69, 255), 1);
			Rect roi(boundRect[i].x, boundRect[i].y, boundRect[i].width, boundRect[i].height);
			imgCrop.push_back(imgThre(roi));
			/*imshow("Image dila", imgThre(roi));
			waitKey(0);*/
		
		}
	}
	reverse(imgCrop.begin(), imgCrop.end());
	return imgCrop;
}
double compare(Mat& img, Mat& temp)
{
	Mat my_temp;
	resize(temp, my_temp, img.size());
	//imshow("pre", my_temp);
	//waitKey(0);
	//
	//imshow("pre", img);
	//waitKey(0);
	//imwrite("600temp.jpg", img);

	int rows, cols;
	uchar* img_point, * temp_point; //像素类型uchar
	rows = my_temp.rows;
	cols = my_temp.cols * img.channels();
	double result, same = 0.0, different = 0.0;
	for (int i = 0; i < rows; i++)       //遍历图像像素
	{
		//获取像素值
		img_point = img.ptr<uchar>(i);
		temp_point = my_temp.ptr<uchar>(i);
		for (int j = 0; j < cols; j++)
		{
			if (img_point[j] == temp_point[j])
				same++;         //记录像素相同的个数
			else
				different++;    //记录像素不同的个数
		}
	}
	result = same / (same + different);
	return result;                     //返回匹配结果
}
float imgDetect(Mat img) {
	Mat clone_img = img.clone();
	cv::GaussianBlur(img, img, cv::Size(3, 3), 0);
	cv::dilate(img, img, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3)));

	vector<vector<Point>> contours; //存储原图轮廓
	vector<Vec4i> hierarcy;
	findContours(img, contours, hierarcy, RETR_EXTERNAL, CHAIN_APPROX_NONE);//查找轮廓
	drawContours(clone_img, contours, -1, Scalar(0, 255, 0), 1, 8);//绘制轮廓
	vector<Rect> sort_rect(contours.size()); //外接矩形
	vector<Mat> img_mat;//存储原图中的有效数字区域
	for (int i = 0; i < contours.size(); i++)
	{
		sort_rect[i] = boundingRect(contours[i]); //外接矩形
		Mat roi = clone_img(sort_rect[i]);
		Mat dstroi;
		resize(roi, dstroi, Size(40, 50), 0, 0); //重设大小
		img_mat.push_back(dstroi);
		//绘制外接矩形
		rectangle(clone_img, Point(sort_rect[i].x, sort_rect[i].y),
			Point(sort_rect[i].x + sort_rect[i].width, sort_rect[i].y + sort_rect[i].height),
			Scalar(0, 0, 255), 1, 8);
	}
	//imshow("pre", clone_img);
	//waitKey(0);

	//对矩形进行排序，因为轮廓的顺序不一定是数字真正的顺序
	for (int i = 0; i < sort_rect.size() - 1; i++)
	{
		for (int j = 0; j < sort_rect.size() - i - 1; j++)
		{
			int j_x = sort_rect[j].x;
			int j1_x = sort_rect[j + 1].x;
			if (j_x > j1_x)
			{
				cv::Rect_<int> tempSize = sort_rect[j];
				sort_rect[j] = sort_rect[j + 1];
				sort_rect[j + 1] = tempSize;

				Mat temps = img_mat[j];
				img_mat[j] = img_mat[j + 1];
				img_mat[j + 1] = temps;
			}
		}
	}

	vector<Mat> myTemplate; //模板容器
	for (int i = 0; i < 11; i++)
	{
		string name = format("img/%d.PNG", i);
		Mat temp = imread(name);
		cvtColor(temp, temp, COLOR_BGR2GRAY);
		threshold(temp, temp, 0, 255, THRESH_BINARY);
		myTemplate.push_back(temp);
		/*imshow("pre", temp);
		waitKey(0);*/
	}



	vector<int> seq;//顺序存放识别结果
	for (int i = 0; i < img_mat.size(); i++)
	{
		double com = 0;
		double min = 0;
		int min_seq = 0;//记录识别结果
		for (int j = 0; j < myTemplate.size(); j++)
		{
			/*imshow("pre", img_mat[i]);
			waitKey(0);
			imshow("pre", myTemplate[j]);
			waitKey(0);*/
			com = compare(img_mat[i], myTemplate[j]);
			if (com > min)
			{
				min = com;
				min_seq = j;
			}
			com = 0;
		}
		seq.push_back(min_seq);
	}
	//输出结果
	float result = 0;
	int j = 1;
	int point = 0;
	for (int i = seq.size() - 1; i >= 0; i--) {
		if (seq[i] < 10) {
			result += seq[i] * j;
			j = j * 10;
		}
		else {
			point = i;
		}
	}

	if (point != 0)result = result * pow(0.1, seq.size() - point - 1);

	return result;
}

bool compareVectors(const std::vector<float>& vec1, const std::vector<float>& vec2) {
	// 如果两个向量大小不同，则它们不相等
	if (vec1.size() != vec2.size()) {
		return false;
	}

	// 逐个比较向量中的元素
	for (size_t i = 0; i < vec1.size(); ++i) {
		if (vec1[i] != vec2[i]) {
			return false;
		}
	}

	// 如果所有元素都相等，则两个向量相等
	return true;
}

六、主函数

void main() {
	int i = 1;
	vector<float> vecResult;
	while (i++) {
		clock_t start, stop;
		
		HWND hq = FindWindow(NULL, TEXT("MainWindow"));
		SavePic(L"1.png", hq);
		Sleep(2640);
		//cv::Mat image2 = cv::imread("4.png");
		/*cv::imshow("meinv", image2);
		cv::waitKey(0);*/

		//string path = to_string(i) + ".png";
		string path = "1.png";
		Mat img = imread(path);//读取图片
		Mat imgGray, imgBlur, imgCanny, imgDila, imgErode, imgThre;
		Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3));
		//preprocessing
		cvtColor(img, imgGray, COLOR_BGR2GRAY, 0);
		threshold(imgGray, imgThre, 0, 255, THRESH_BINARY);
		GaussianBlur(imgThre, imgBlur, Size(65, 65), 1, 1);
		Canny(imgBlur, imgCanny, 40, 120);
		dilate(imgCanny, imgDila, kernel);
		//灰度->高斯滤波->Canny边缘算法->膨胀
		vector<Mat> imgFinal = getcontour(imgDila, imgThre);
		/*imshow("Image", img);
		imshow("Image Gray", imgGray);
		imshow("Image Blur", imgBlur);
		imshow("Image Canny", imgCanny);
		imshow("Image dila", imgDila);
		waitKey(0);*/

		/*clock_t start, stop;
		start = clock();*/


		float result = 0;
		
		start = clock();
		vector<float> vecResult2(vecResult.begin(), vecResult.end());
		vecResult.clear();
		for (int i = 0; i < imgFinal.size(); i++) {
			result = imgDetect(imgFinal[i]);
			cout << "识别结果为：" << result << endl;
			
			vecResult.push_back(result);
		}
		
		if (!compareVectors(vecResult, vecResult2)) {
			for (int i = 0; i < vecResult.size(); i++) {
				string filePath = "result.txt";
				ofstream outfile(filePath,ios::app);
				outfile.is_open();
				if (!outfile)
				{
					cout << "打开文件失败" << endl;
				}
				outfile << vecResult[i] << endl;
			}
		}
		stop = clock();
		cout << "识别时间:" << stop - start << "ms" << endl;
	}
}

七. 结果展示

        测试数据约10000个，准确率高达100%。

总结

        希望后生能好好为老师服务，别当一个不负责的人

悦读

道可道，非常道；名可名，非常名。 无名，天地之始，有名，万物之母。 故常无欲，以观其妙，常有欲，以观其徼。 此两者，同出而异名，同谓之玄，玄之又玄，众妙之门。

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