c++和opencv针对图像处理的API接口直接调用即可

都是自己辛辛苦苦编写的，转载注意备注

quickopencv.h

#pragma once
#include
using namespace cv;
typedef unsigned char BYTE;

class QuickDemo {
public:
	//输入一张图，就会完成一系列的Demo操作
	void colorSpace_Demo(Mat &image);
	void pixel_visit_demo(Mat &image);
	void pixel_operators_demo(Mat &image);
	void bitwise_demo(Mat &image);
	void channels_demo(Mat &image);
	void inrange_demo(Mat &image);
	void pixel_statistic_demo(Mat &image);
	void drawing_demo(Mat &image);
	void random_drawing_demo();
	void mouse_drawing_demo(Mat &image);
	void histogram_demo(Mat &image);
	Mat histogram_grayImage(const Mat& image);
	void histogram_2d_demo(Mat &image);
	void norm_demo(Mat &image);
	void resize_demo(Mat &image);
	void resize_fixed_demo();
	void flip_demo(Mat &image);
	void rotate_demo(Mat &image);
	void video_demo(Mat &image);
	void histograme_eq_demo(Mat &image);
	void blur_demo(Mat &image);
	void gaussian_blur_demo(Mat &image);
	void bifilter_demo(Mat &image);
	void face_detection_demo();//不加形参是因为想做一个视频版的
	void elementsizechange_open_demo(Mat& image);
	void elementsizechange_close_demo(Mat& image);
	void elementsizechange_gradient_demo(Mat& image);
	void elementsizechange_tophat_demo(Mat& image);
	void elementsizechange_blackhat_demo(Mat& image);
	void draw_ROI_histogram_demo(Mat& image);
	void template_match_demo(Mat *pTo,Mat *pTemplate,Mat *src);
	void pic_masking1_way1_demo(Mat& image);//图像的掩模操作way1
	void pic_masking_way2_demo(Mat& image);//图像的掩模操作way2
	void read_write_piexl_demo(Mat& image);
	void image_blending_demo();//图像混合
	void adjust_brightness_and_contrast_demo(Mat& image);
	void image_blur_demo(Mat& image);//图像模糊
	void image_filtering_demo(Mat& image);
	void convexhull_detect_demo();
	Mat gray_histogram(Mat& image);
	Mat grayImage_mask_print(Mat &image);//灰色图像按照灰度值进行掩模
	/*
		用红色Mask出一个图片中RGB三个通道像素值都符合在某一个范围内图像区域，用蓝色mask出既符合三通道
		像素值在一个范围内也符合亮度在一个区间内的局部区域,并输出符合区域所占整个区域的比例
	*/
	Mat mask_localImage(Mat& image);
	/*给图像加边界框*/

	void draw_interest_pluse(Mat& image, int re_h, int re_w);

	void histogram_local_image(Mat& image);//返回局部区域直方图
	void draw_interset(Mat& image_add);//把图像中颜色为红色部分画矩形框圈起来
	void image_mask(Mat& image, Mat& heightMat);//根据图像高度画ROI区域把高度大于1000的掩模为红色
	void image_polar_mask_demo(Mat& image, Mat& heightMat); //图像极性标定

	void lead_box(Mat& image);//利用leadbox算法对引脚的缺陷进行检测。首先定位引脚的检测窗口，再定位每个引脚的头部和尾部

	Mat lead_mask(Mat& image,Mat &gray_image);//对引脚的ROI主选窗口区域掩模为红色
	Mat sub_lead_mask(Mat& image, Mat& gray_image);//对RGB值和亮度在一定范围内的引脚进行标记

	Mat min_box(Mat& rgb_image, Mat& after_mask);对ROI大区域内的每个引脚进行画标记框

	Mat unevenLightCompensate(Mat& image, int blockSize);//对于光度不均匀处理

	Mat binary_image(Mat &image);
};

 quickdemo.cpp

#include
#include
#include
#include

using namespace cv;
using namespace std;

//::表示引用自己创建的头文件方法
void QuickDemo::colorSpace_Demo(Mat &image) {
	Mat gray, hsv;
	cvtColor(image, hsv, COLOR_BGR2HSV);
	cvtColor(image, gray, COLOR_BGR2GRAY);
	imshow("HSV", hsv);
	imshow("灰度", gray);
	imwrite("D://hsv.png", hsv);
	imwrite("D://gray.png", gray);
}
void QuickDemo::pixel_visit_demo(Mat &image) {
	int w = image.cols;
	int h = image.rows;
	int dims = image.channels();
	//遍历图像的像素，灰度图是一个通道，而彩色图是三个通道，因此遍历的方式是不同的
	/*
	for (int row = 0; row < h; row++) {
		for (int col = 0; col < w; col++) {
			//在这里要判断通道数，因为不同通道数对于图像像素遍历不同
			if (dims == 1) {
				//用下面方式就把原来的像素uchar类型的转化为int类型
				int pv = image.at(row, col);
				//对像素值进行取反
				image.at(row, col) = 255 - pv;
			}
			if (dims == 3) {
				//对于彩色图片一个位置存放三个通道的三个数
				Vec3b bgr = image.at(row, col);
				//各个通道的的数据取反为如下所示：
				image.at(row, col) = 255 - bgr[0];
				image.at(row, col) = 255 - bgr[1];
				image.at(row, col) = 255 - bgr[2];
			}
		}
	}
	*/
	//下面我们用指针的方式进行实现，给每一行一个指针
	for (int row = 0; row < h; row++) {
		//指针是uchar类型
		uchar* current_row = image.ptr(row);
		for (int col = 0; col < w; col++) {
			//在这里要判断通道数，因为不同通道数对于图像像素遍历不同
			if (dims == 1) {
				//用下面方式就把原来的像素uchar类型的转化为int类型
				int pv = *current_row;
				//对像素值进行取反
				*current_row++ = 255 - pv;
			}
			if (dims == 3) {
				//对于彩色图片一个位置存放三个通道的三个数
				Vec3b bgr = image.at(row, col);
				//各个通道的的数据取反为如下所示：
				*current_row++ = 255 - bgr[0];
				*current_row++ = 255 - bgr[1];
				*current_row++ = 255 - bgr[2];
			}
		}
	}

	//像素的读写显示
	imshow("像素读写显示", image);
}

void QuickDemo::pixel_operators_demo(Mat &image){
	Mat dst;
	dst = image + Scalar(50, 50, 50);
	imshow("像素加法操作", dst);
}

void QuickDemo::bitwise_demo(Mat &image) {
	Mat m1 = Mat::zeros(Size(256, 256), CV_8UC3);
	Mat m2 = Mat::zeros(Size(256, 256), CV_8UC3);
	//对于m1图像画矩形，左上角的坐标是（100，100），矩形的大小是（80*80），矩形的颜色scalar是（255，255，0）
	//thickness参数大于0表示绘制矩形，小于0表示填充矩形
	rectangle(m1, Rect(100, 100, 80, 80), Scalar(255,255,0),-1, LINE_8, 0);
	rectangle(m2, Rect(150, 150, 80, 80), Scalar(0, 255, 255), -1, LINE_8, 0);
	imshow("m1", m1);
	imshow("m2", m2);
}

//主要完成通道分离与通道合并
void QuickDemo::channels_demo(Mat &image) {
	//由于有多个通道，所以用集合容器,把多个通道放进容器
	std::vector mv;
	split(image, mv);
	imshow("蓝色", mv[0]);
	imshow("绿色", mv[1]);
	imshow("红色", mv[2]);

	Mat dst;
	mv[1] = 0;
	mv[2] = 0;
	merge(mv, dst);
	imshow("蓝色", dst);
}

void QuickDemo::inrange_demo(Mat& image) {
	Mat hsv;
	cvtColor(image, hsv, COLOR_BGR2HSV);
	Mat mask;
	//inRange()函数OpenCV中的inRange()函数可实现二值化功能,更关键的是可以同时针对多通道进行操作，使用起来非常方便！
	//主要是将在两个阈值内的像素值设置为白色（255），而不在阈值区间内的像素值设置为黑色（0），该功能类似于之间所讲的双阈值化操作。
	//第一个Scalar是绿色hsv三个参数的最小值，第二个是最大值，可以把背景为绿色的去掉，转化为二值化图像
	inRange(hsv, Scalar(35,43,46), Scalar(77,255,255), mask);
	imshow("mask", mask);
}


void QuickDemo::pixel_statistic_demo(Mat& image) {
	double minv, maxv;
	Point minLoc, maxLoc;
	std::vector mv;
	split(image, mv);
	for (int i = 0; i < mv.size(); i++) {
		minMaxLoc(mv[i], &minv, &maxv, &minLoc, &maxLoc, Mat());
		std::cout << "min value:" << minv << "max value:" << maxv << std::endl;
	}
	//求取均值和方差
	Mat mean, stddev;
	meanStdDev(image, mean, stddev);
	std::cout << "means:" << mean << "stddev:" << stddev << std::endl;
}

void QuickDemo::drawing_demo(Mat& image) {
	Rect rect;
	rect.x = 200;
	rect.y = 200;
	rect.width = 250;
	rect.height = 300;
	//生成一个模板，我们想在bg上面绘制
	Mat bg = Mat::zeros(image.size(), image.type());
	rectangle(bg, rect, Scalar(0, 0, 255),2, 8, 0);
	//在图像（350，400）的位置画一个半径为15的圆，颜色为蓝
	circle(bg, Point(350,400),15,Scalar(255,0,0),2,0);
	Mat dst;
	addWeighted(image, 0.7, bg, 0.3, 0, dst);
	imshow("绘制演示", bg);
}
void QuickDemo::random_drawing_demo() {
	//随机绘制
	Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3);
	int w = canvas.cols;
	int h = canvas.rows;
	RNG rng(12345);//OpenCV随机数产生器，12345是随机数种子，默认产生的是系统时间
	while(true){//无限循环一直画
		//键盘的响应操作，只有按键盘的ESC键退出才不会一直画，否则会一直画线
		int c = waitKey(10);
		if (c == 27) {
			break;
		}
		//线段有两个点，设定点的X，Y坐标
		int x1 = rng.uniform(0, w);//产生统一分布的a,b之间的数
		int y1 = rng.uniform(0, h);
		int x2 = rng.uniform(0, w);
		int y2 = rng.uniform(0, h);

		//颜色也随机产生
		int b = rng.uniform(0, 255);
		int g = rng.uniform(0, 255);
		int r = rng.uniform(0, 255);

		line(canvas, Point(x1, y1), Point(x2, y2), Scalar(b, g, r), 1, LINE_4, 0);
		imshow("随机错误演示：", canvas);
	}
}

Point sp(-1, -1); //鼠标开始的位置
Point ep(-1, -1);//鼠标结束的位置
Mat temp;
static void on_draw(int event, int x, int y, int flag, void* userdata) {
	Mat image = *((Mat*)userdata);
	if (event == EVENT_LBUTTONDOWN) {//左键点击
		sp.x = x;
		sp.y = y;
		std::cout << "start point" << sp << std::endl;
	}
	else if (event == EVENT_LBUTTONUP) {//左键放开
		ep.x = x;
		ep.y = y;
		int dx = ep.x - sp.x;
		int dy = ep.y - sp.y;
		if (dx > 0 && dy > 0) {
			Rect box(sp.x, sp.y, dx, dy);//可以用鼠标画矩形，但是我们希望在图像上画，所以需要添加如下代码
			rectangle(image, box, Scalar(0, 0, 255), 2, 8, 0);
			imshow("鼠标绘制显示：", image);//绘制了要急时更新
			//一个矩形绘制好了以后要把鼠标的初始位置还是置为-1，为了下一次绘制做准备
			sp.x = -1;
			sp.y = -1;
		}
	}
	else if (event == EVENT_MOUSEMOVE) {
		if (sp.x > 10 && sp.y > 0) {//表示鼠标的左键要按下去
			ep.x = x;
			ep.y = y;
			int dx = ep.x - sp.x;
			int dy = ep.y - sp.y;
			if (dx > 0 && dy > 0) {
				Rect box(sp.x, sp.y, dx, dy);
				temp.copyTo(image);
				imshow("ROI区域", image(box));
				rectangle(image, box, Scalar(0, 0, 255), 2, 8, 0);
				imshow("鼠标绘制", image);
			}
		}

	}
}
void QuickDemo::mouse_drawing_demo(Mat &image) {
	//鼠标的基本操作：使用鼠标画图
	namedWindow("鼠标绘制", WINDOW_AUTOSIZE);
	setMouseCallback("鼠标绘制", on_draw, (void*)(&image));
	imshow("鼠标绘制显示：", image);
	temp = image.clone();//把图像保存在这边
}

void QuickDemo::norm_demo(Mat &image) {
	Mat dst;
	image.convertTo(dst,CV_32F);//把类型转化为float类型
	std::cout << image.type() << dst.type() << std::endl;
	//转化为float类型后就可以使用归一化了
	normalize(image, dst, 1.0, 0, NORM_MINMAX);
	imshow("图像归一化", dst);
}

void QuickDemo::resize_demo(Mat &image) {
	Mat zoomin, zoomout;//定义放大和缩小
	int h = image.rows;
	int w = image.cols;
	resize(image,zoomin,Size(h/2,w/2),0,0,INTER_LINEAR);//0,0这两个参数表示水平方向和数值方向缩放的比率
	imshow("zoomin", zoomin);
	//放大
	resize(image, zoomout, Size(3*h / 2, 3*w / 2), 0, 0, INTER_LINEAR);//0,0这两个参数表示水平方向和数值方向缩放的比率
	imshow("zoomout", zoomout);
}

void QuickDemo::resize_fixed_demo() {
	Mat src;
	string path1 = "D:\\testImage\\";
	string path2 = "D:\test_0data\\";
	string pp = ".jpg";
	for (int i = 1; i < 4; i++)
	{
		string c;
		c = to_string(i);
		string path = path1 + c + pp;
		src = imread(path, 1);
		resize(src, src, Size(120, 120));
		string pathh = path2 + c + pp;
		imwrite(pathh, src);
	}

}


void QuickDemo::flip_demo(Mat &image) {
	Mat dst;
	flip(image, dst, 0);//参数0表示上下反转，1左右反转，-1表示180度旋转
	imshow("图像反转", dst);
}

void QuickDemo::rotate_demo(Mat& image) {
	//旋转
	Mat dst, M;//dst输出，M是旋转矩阵
	int w = image.cols;
	int h = image.rows;
	M = getRotationMatrix2D(Point2f(w / 2, h / 2), 45, 1.0);//获得旋转矩阵，参数为矩阵中心，旋转角度，尺寸是否变化，1就是不变
	warpAffine(image, dst, M, image.size());
	imshow("旋转演示", dst);
}

void QuickDemo::video_demo(Mat& image) {
	//VideoCapture capture(0); //获取摄像头文件，初始化对象，把0换成视频文件就可以传入需要处理的视频
	VideoCapture capture("D:\video_data");
	//获取frame的宽高
	int frame_width = capture.get(CAP_PROP_FRAME_WIDTH);
	int frame_height = capture.get(CAP_PROP_FRAME_HEIGHT);
	int frame_count = capture.get(CAP_PROP_FRAME_COUNT);
	double fps = capture.get(CAP_PROP_FPS);
	std::cout << "frame width:" << frame_width << std::endl;//标准的输入输出
	std::cout << "frame height:" << frame_height << std::endl;
	std::cout << "frame count:" << frame_count << std::endl;
	std::cout << "FPS" << fps << std::endl;
	//将处理好的视频的保存
	VideoWriter writer("D:/test.mp4",capture.get(CAP_PROP_FOURCC),fps,Size(frame_width,frame_height),true);
	Mat frame;
	while (true) {
		capture.read(frame);
		flip(frame, frame, 1);
		if (frame.empty()) {
			break;
		}
		imshow("frame", frame);
		//这里的下面可以对视频做一些前面讲的操作
		//例如做一个色彩空间的转换
		//To do something......
		colorSpace_Demo(frame);
		//视频处理好了开始保存
		writer.write(frame);
		//视频需要键盘操作停顿
		int c = waitKey(10);
		if (c == 27) {//退出
			break;
		}
	}
	//视频做完了一定要释放
	capture.release();
	writer.release();
}



void QuickDemo::histogram_demo(Mat& image) {
	/*图像直方图是图像像素值的统计学特征，计算代价较小，具有图像的平移、旋转、缩放不变性的优点。
	Bins是指直方图的大小范围
	*/
	//三通道分离
	std::vectorbgr_plane;
	split(image, bgr_plane);
	//定义参数变量
	const int channels[1] = { 0 };
	const int bins[1] = { 256 };//一共有256个灰度级别
	float hranges[2] = { 0,255 };//每个通道的灰度级别是0-255
	const float* ranges[1] = { hranges };
	Mat b_hist;
	Mat g_hist;
	Mat r_hist;
	//计算Blue、Green、Red通道的直方图,1表示只有一张图，因为可以支持多张图多个通道；0表示只有1个通道；raanges就是直方图的取值范围0-25
	calcHist(&bgr_plane[0], 1, 0, Mat(), b_hist, 1, bins, ranges);
	calcHist(&bgr_plane[1], 1, 0, Mat(), g_hist, 1, bins, ranges);
	calcHist(&bgr_plane[2], 1, 0, Mat(), r_hist, 1, bins, ranges);
	//显示直方图
	int hist_w = 512;
	int hist_h = 400;
	int bin_w = cvRound((double)hist_w / bins[0]);
	Mat histImage = Mat::zeros(hist_h, hist_w, CV_8UC3);
	//归一化直方图数据
	normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());
	normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());
	normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());
	//绘制直方图曲线
	for (int i = 1; i < bins[0]; i++) {
		line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(b_hist.at(i - 1))),
			Point(bin_w * (i), hist_h - cvRound(b_hist.at(i))), Scalar(255, 0, 0), 2, 8, 0);
		line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(g_hist.at(i - 1))),
			Point(bin_w * (i), hist_h - cvRound(g_hist.at(i))), Scalar(0, 255, 0), 2, 8, 0);
		line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(r_hist.at(i - 1))),
			Point(bin_w * (i), hist_h - cvRound(r_hist.at(i))), Scalar(0, 0, 255), 2, 8, 0);
	}
	//显示直方图
	namedWindow("Histogram Demo", WINDOW_AUTOSIZE);
	imshow("Histogram Demo", histImage);
}

Mat QuickDemo::histogram_grayImage(const Mat& image)
{
	//定义求直方图的通道数目，从0开始索引
	int channels[] = { 0 };
	//定义直方图的在每一维上的大小，例如灰度图直方图的横坐标是图像的灰度值，就一维，bin的个数
	//如果直方图图像横坐标bin个数为x，纵坐标bin个数为y，则channels[]={1,2}其直方图应该为三维的，Z轴是每个bin上统计的数目
	const int histSize[] = { 256 };
	//每一维bin的变化范围
	float range[] = { 0,256 };

	//所有bin的变化范围，个数跟channels应该跟channels一致
	const float* ranges[] = { range };

	//定义直方图，这里求的是直方图数据
	Mat hist;
	//opencv中计算直方图的函数，hist大小为256*1，每行存储的统计的该行对应的灰度值的个数
	calcHist(&image, 1, channels, Mat(), hist, 1, histSize, ranges, true, false);

	//找出直方图统计的个数的最大值，用来作为直方图纵坐标的高
	double maxValue = 0;
	//找矩阵中最大最小值及对应索引的函数
	minMaxLoc(hist, 0, &maxValue, 0, 0);
	//最大值取整
	int rows = cvRound(maxValue);
	//定义直方图图像，直方图纵坐标的高作为行数，列数为256(灰度值的个数)
	//因为是直方图的图像，所以以黑白两色为区分，白色为直方图的图像
	Mat histImage = Mat::zeros(rows, 256, CV_8UC1);

	//直方图图像表示
	for (int i = 0; i < 256; i++)
	{
		//取每个bin的数目
		int temp = (int)(hist.at(i, 0));
		//如果bin数目为0，则说明图像上没有该灰度值，则整列为黑色
		//如果图像上有该灰度值，则将该列对应个数的像素设为白色
		if (temp)
		{
			//由于图像坐标是以左上角为原点，所以要进行变换，使直方图图像以左下角为坐标原点
			histImage.col(i).rowRange(Range(rows - temp, rows)) = 255;
		}
	}
	//由于直方图图像列高可能很高，因此进行图像对列要进行对应的缩减，使直方图图像更直观
	Mat resizeImage;
	resize(histImage, resizeImage, Size(256, 256));
	return resizeImage;
	
}

void QuickDemo::histogram_2d_demo(Mat& image) {
	//2D直方图
	Mat hsv, hs_hist;
	cvtColor(image, hsv, COLOR_BGR2HSV);
	int hbins = 30;//H一共有180，设置hbins为30可以理解为分30个类统计
	int sbins = 32;
	int hist_bins[] = { hbins,sbins };
	float h_range[] = { 0,180 };
	float s_range[] = { 0,256 };
	const float* hs_ranges[] = { h_range,s_range };
	int hs_channels[] = { 0,1 };
	calcHist(&hsv, 1, hs_channels, Mat(), hs_hist, 2, hist_bins, hs_ranges, true, false);
	double maxVal = 0;
	minMaxLoc(hs_hist, 0, &maxVal, 0, 0);
	int scale = 10;
	Mat hist2d_image = Mat::zeros(sbins * scale, hbins * scale, CV_8UC3);
	for (int h = 0; h < hbins; h++) {
		for (int s = 0; s < sbins; s++) {
			float binVal = hs_hist.at(h, s);
			int intensity = cvRound(binVal * 255 / maxVal);
			rectangle(hist2d_image, Point(h * scale, s * scale), Point((h + 1) * scale - 1, (s + 1) * scale - 1), Scalar::all(intensity), -1);
		}

	}
	applyColorMap(hist2d_image, hist2d_image, COLORMAP_JET);
	imshow("H-S Histogram", hist2d_image);
	imwrite("D:/hist_2d.png", hist2d_image);

}

void QuickDemo::histograme_eq_demo(Mat &image) {
	//图像均衡化只支持灰度图像
	Mat gray;
	cvtColor(image,gray, COLOR_BGR2GRAY);
	imshow("灰度图像：", gray);
	Mat dst;
	equalizeHist(gray, dst);
	imshow("直方图均衡化演示：", dst);
}

void QuickDemo::blur_demo(Mat& image) {//卷积使得模糊
	Mat dst;
	blur(image, dst, Size(13, 13), Point(-1,-1));//卷积核3*3,卷积核的中心位置作为输出点
	imshow("图像模糊", dst);
}

void QuickDemo::gaussian_blur_demo(Mat &image) {
	Mat dst;
	GaussianBlur(image, dst, Size(5, 5),15);//Gaussian的窗口大小为5*5，Sigma是15
	imshow("高斯模糊", dst);
}

void QuickDemo::bifilter_demo(Mat &image) {
	Mat dst;
	bilateralFilter(image, dst, 0, 100, 10);
	imshow("双边模糊", dst);
}

void QuickDemo::face_detection_demo() {
	std::string root_dir = "D:/opencv/opencv/sources/samples/dnn/ace_detector/";
	//读取深度学习网络
	dnn::Net net = dnn::readNetFromTensorflow(root_dir+"opencv_face_detector_uint8.pb",root_dir+"opencv_face_detector.pbtxt");
	//网络模型加载好了我们需要加载视频
	VideoCapture capture("D:/opencv/opencv/sources/samples/data/vtest.avi");
	Mat frame;
	while (true) {
		capture.read(frame);
		if (frame.empty()) {
			break;
		}
		//To do something.....
		Mat blob = dnn::blobFromImage(frame, 1.0, Size(300, 300), Scalar(104, 177, 123), false, false);
		net.setInput(blob);
		Mat probs = net.forward();
		Mat detectionMat(probs.size[2], probs.size[3], CV_32F, probs.ptr());
		//解析结果
		for (int i = 0; i < detectionMat.rows; i++) {
			float confidence = detectionMat.at(i,2);
			if (confidence > 0.5) {
				//得到矩形的宽高
				int x1 = static_cast(detectionMat.at(i, 3) * frame.cols);
				int y1 = static_cast(detectionMat.at(i, 4) * frame.rows);
				int x2 = static_cast(detectionMat.at(i, 5) * frame.cols);
				int y2 = static_cast(detectionMat.at(i, 6) * frame.rows);
				Rect box(x1, y1, x2 - x1, y2 - y1);
				rectangle(frame, box, Scalar(0, 0, 255), 2, 8, 0);
			}


		}
		imshow("人脸检测显示", frame);
		int c = waitKey(1);
		if (c == 27) {//退出
			break;
		}
	}

}

void QuickDemo::elementsizechange_open_demo(Mat& image) {
	//开操作
	Mat dst;
	int elementsize = 2;
	Mat kernel = getStructuringElement(MORPH_RECT, Size(elementsize * 2 + 1, elementsize * 2 + 1));//保证是奇数
	morphologyEx(image, dst, MORPH_OPEN, kernel);
	namedWindow("开操作后", WINDOW_FREERATIO);
	imshow("开操作后", dst);
}

void QuickDemo::elementsizechange_close_demo(Mat& image) {
	//闭操作
	Mat dst;
	int elementsize = 2;
	Mat kernel = getStructuringElement(MORPH_RECT, Size(elementsize * 2 + 1, elementsize * 2 + 1));
	morphologyEx(image, dst, MORPH_CLOSE, kernel);
	namedWindow("闭操作以后", WINDOW_AUTOSIZE);
	imshow("闭操作以后", dst);
}

void QuickDemo::elementsizechange_gradient_demo(Mat& image) {
	//形态学梯度
	Mat dst;
	int elementsize = 3;
	Mat kernel = getStructuringElement(MORPH_RECT, Size(elementsize * 2 + 1, elementsize * 2 + 1));
	morphologyEx(image, dst, MORPH_GRADIENT, kernel);
	imshow("GRADIENT", dst);
}

void QuickDemo::elementsizechange_tophat_demo(Mat& image) {
	//顶帽操作
	Mat dst;
	int elementsize = 3;
	Mat kernel = getStructuringElement(MORPH_RECT, Size(elementsize * 2 + 1, elementsize * 2 + 1));
	morphologyEx(image, dst, MORPH_TOPHAT, kernel);
	imshow("TOPHAT", dst);

}
void QuickDemo::elementsizechange_blackhat_demo(Mat& image) {
	Mat dst;
	int elementsize = 3;
	Mat kernel = getStructuringElement(MORPH_RECT, Size(elementsize * 2 + 1, elementsize * 2 + 1));
	morphologyEx(image, dst, MORPH_BLACKHAT, kernel);
	imshow("BLACKHAT", dst);
}


void QuickDemo::template_match_demo(Mat* pTo, Mat* pTemplate, Mat* src) {
	//模板匹配
	int i, j, m, n;
	double dSumT;//模板元素的平均和
	double dSumS;//图像子区域元素的平均和
	double dSumST;//图像子区域和模板的点积
	double R;//响应值
	double MaxR;//记录当前的最大响应
	//最大响应出现的位置
	int nMaxX;
	int nMaxY;
	int nHeight = src->rows;
	int nwidth = src->cols;
	//模板的高和宽
	int nTplHeight = pTemplate->rows;
	int nTplwidth = pTemplate->cols;
	//计算dSumT
	dSumT = 0;
	for (m = 0; m < nTplHeight; m++) {
		for (n = 0; n < nTplwidth; n++) {
			//模板图像第m行，第n个像素的灰度值
			int nGray = *pTemplate->ptr(m, n);
			dSumT += (double)nGray * nGray;
		}
	}
	//找到图像中最大响应的出现位置
	MaxR = 0;
	for (i = 0; i < nHeight - nTplHeight + 1; i++) {
		for (j = 0; j < nwidth - nTplwidth + 1; j++) {
			dSumST = 0;
			dSumS = 0;
			for (m = 0; m < nTplHeight; m++) {
				for (n = 0; n < nTplwidth; n++) {
					// 原图像第i+m行，第j+n列象素的灰度值
					int nGraySrc = *src->ptr(i + m, j + n);
					//模板图像第m行，第n个像素的灰度值
					int nGrayTp1 = *pTemplate->ptr(m, n);
					dSumS += (double)nGraySrc * nGraySrc;
					dSumST += (double)nGraySrc * nGrayTp1;
				}
			}
			R = dSumS / (sqrt(dSumS) * sqrt(dSumT));//计算相应响应
			//与最大相似性比较
			if (R > MaxR) {
				MaxR = R;
				nMaxX = j;
				nMaxY = i;
			}
		}
		//将找到的最佳匹配区域复制到模板图像
		for (int m=0; m < nTplHeight; m++) {
			for (n = 0; n < nTplwidth; n++) {
				int nGray = *src->ptr(nMaxY + m, nMaxX + n);
				//pTo->setTo(nMaxX + n, nMaxY + m, RGB(nGray, nGray, nGray));
				pTo->at(nMaxY + m, nMaxX + n) = nGray;
			}
		}
	}
}

void QuickDemo::pic_masking1_way1_demo(Mat& image) {
	int cols = (image.cols - 1)* image.channels();//获取图像的宽；
	int offsetx = image.channels();
	int rows = image.rows;
	Mat dst = Mat::zeros(image.size(), image.type());
	for (int row = 1; row < rows - 1; row++) {//是从（1，1）位置开始的，而不是从0位置开始的
		const uchar* current = image.ptr(row);
		const uchar* previous = image.ptr(row-1);
		const uchar* nest = image.ptr(row+1);
		uchar* output = dst.ptr(row);
		for (int col = offsetx; col < cols; col++) {
			output[col] = saturate_cast(5 * current[col] - (current[col - offsetx] + current[col + offsetx] + nest[col]));
		}
	}
	imshow("contrast_image", dst);
}

void QuickDemo::pic_masking_way2_demo(Mat& image) {
	Mat dst;
	Mat kernel = (Mat_(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
	filter2D(image, dst, image.depth(), kernel);
	imshow("contrast_image",dst);
}

void QuickDemo::read_write_piexl_demo(Mat& image) {
	Mat gray_image;
	cvtColor(image, gray_image, COLOR_BGR2GRAY);
	imshow("gray_image", gray_image);
	int height = gray_image.rows;
	int width = gray_image.cols;
	for (int row = 0; row < height; row++) {
		for (int col = 0; col < width; col++) {
			int gray = gray_image.at(row, col);//获取每行每列的像素值
			//我们可以改变像素显示反差之后的图像
			gray_image.at(row, col) = 255 - gray;
		}
	}
	imshow("反差图像", gray_image);
}

void QuickDemo::image_blending_demo() {
	Mat image1, image2, dst;
	image1 = imread("D:\\testImage\\12.jpg");
	image2 = imread("D:\\testImage\\2.jpg");
	if (!image1.data) {
		cout << "image1图像不存在" << endl;
	}
	if (!image2.data) {
		cout << "image2图像不存在" << endl;
	}
	if (image1.rows == image2.rows && image1.cols == image2.cols && image1.type() == image2.type()) {
		//进行图像融合
		double alpha = 0.5;
		addWeighted(image1, alpha, image2, (1.0 - alpha), 0.0, dst);
		imshow("image_blending", dst);
	}
	else {
		cout << "could not blend image,the size of images is not same" << endl;
	}

}

void QuickDemo::adjust_brightness_and_contrast_demo(Mat& image) {
	int height = image.rows;
	int width = image.cols;
	//生成一张和原图大小相同的空白图
	Mat dst;
	dst = Mat::zeros(image.size(), image.type());
	float alpha = 1.2;
	float belta = 30;
	for (int row = 0; row < height; row++) {
		for (int col = 0; col < width; col++) {
			if (image.channels() == 3) {
				float b = image.at(row, col)[0];
				float g = image.at(row, col)[1];
				float r = image.at(row, col)[2];
				dst.at(row, col)[0] = saturate_cast(b*alpha+belta);
				dst.at(row, col)[1] = saturate_cast(g * alpha + belta);
				dst.at(row, col)[2] = saturate_cast(r * alpha + belta);
			}
			else if (image.channels() == 1) {
				float v = image.at(row, col);
			}
		}
	}
	imshow("adjust_brightness_and_contrast_image", dst);
}

void QuickDemo::image_blur_demo(Mat& image) {
	Mat dst;
	blur(image, dst, Size(3, 3), Point(-1, -1));
	imshow("中值模糊", dst);
	GaussianBlur(image, dst, Size(5, 5), 11, 11);
	imshow("高斯模糊",dst);
}

void QuickDemo::image_filtering_demo(Mat& image) {
	Mat dst;
	medianBlur(image, dst, 3);
	imshow("中值滤波", dst);
	bilateralFilter(image, dst, 15,150,3);//15是窗口大小，
	imshow("双边滤波", dst);
}



Mat QuickDemo::gray_histogram(Mat& image)
{
	double maxValue = 0;
	double minValue = 0;
	for (int i = 0; i < image.rows; i++)
	{
		for (int j = 0; j < image.cols; j++) 
		{
			int b = (int)image.at(i, j);
			cout << b << endl;
			if (b > 20 && b < 200) {
			
			}
		}
		
	}
	
	minMaxLoc(image, &minValue, &maxValue, 0, 0);//找到全局最小、最大的像素值数目
	//cout << "max: " << maxValue << "min: " << minValue << endl;
	int imageSize = image.rows;
	Mat histImage(imageSize, imageSize, CV_8UC3, Scalar(255, 255, 255));
	int hpt = static_cast(0.9 * imageSize);
	int total = 0;
	Scalar color(172, 172, 150);//BGR
	for (int h = 0; h < imageSize; h++)
	{
		float binVal = image.at(h);//读取对应灰度级的像素个数
		//cout << h << ":" << binVal << endl;
		total += binVal;
		int intensity = static_cast(binVal * hpt / maxValue);
		//line(histImage, Point(h, imageSize), Point(h, imageSize - intensity), color);
		rectangle(histImage, Point(h, imageSize), Point(h + 1, imageSize - intensity), color);

		/*cout << total;*/
	}

	return histImage;

}

Mat QuickDemo::grayImage_mask_print(Mat& image)
{
	// 输入图像image是单通道的灰度图
	int h = image.rows;
	int w = image.cols;
	int dims = image.channels();

	// 将单通道灰度图转换成为伪灰度图的三通道图像
	Mat image_bgr;
	cvtColor(image, image_bgr, COLOR_GRAY2BGR);

	for (int row = 0; row < h; row++) 
	{
		for (int col = 0; col < w; col++) {
			if (image.at(row, col) > 0 && image.at(row, col) < 40) {
				image_bgr.at(row, col)[0] = 0;
				image_bgr.at(row, col)[1] = 0;
				image_bgr.at(row, col)[2] = 255;
			}
		}
	}
	return image_bgr;
	
}

Mat QuickDemo::mask_localImage(Mat& image)
{
	// 输入图像image是单通道的灰度图
	float h = image.rows;
	float w = image.cols;
	int dims = image.channels();

	// 将单通道灰度图转换成为伪灰度图的三通道图像
	Mat image_bgr1;
	cvtColor(image, image_bgr1, COLOR_GRAY2BGR);
	int r_count = 0;

	for (int row = 0; row < h; row++)
	{
		for (int col = 0; col < w; col++) 
		{
			if (image_bgr1.at(row, col)[0] > 10 && 
				image_bgr1.at(row, col)[1] > 100 && 
				image_bgr1.at(row, col)[2]>150)
			{
				image_bgr1.at(row, col)[0] = 0;
				image_bgr1.at(row, col)[1] = 0;
				image_bgr1.at(row, col)[2] = 255;
				r_count++;

			}
			else if (image.at(row,col)>50 && 
				image.at(row, col)<100)
			{
				image_bgr1.at(row, col)[0] = 0;
				image_bgr1.at(row, col)[1] = 255;
				image_bgr1.at(row, col)[2] = 0;
			}
			else if (image_bgr1.at(row, col)[0] > 10 && 
				image_bgr1.at(row, col)[1] > 100 && 
				image_bgr1.at(row, col)[2] > 150
				&& image.at(row, col) > 50 ||
				image.at(row, col) < 100)
			{
				image_bgr1.at(row, col)[0] = 255;
				image_bgr1.at(row, col)[1] = 0;
				image_bgr1.at(row, col)[2] = 0;
			}
			
		}
		
	}
	cout << "红色区域的像素个数" << r_count << endl;
	cout << "红色区域的像素所占的比率是：" << r_count / (h * w) << endl;
	return image_bgr1;
}

void QuickDemo::draw_interest_pluse(Mat& image, int re_h, int re_w) {
	// 首先打上掩膜
	Mat image_add = mask_localImage(image);
	int h = image_add.rows; // 原图高
	int w = image_add.cols; // 原图宽
	int r_h = h / re_h; // 分割高度以后的区域
	int r_w = w / re_w; // 分割宽度以后的区域
	// vector points; // 设置容器，用来存储坐标
	for (int i = 0; i < re_h; i++) {
		for (int j = 0; j < re_w; j++) {
			vector points; // 设置容器，用来存储坐标
			for (int row = i * r_h; row < r_h * (i + 1); row++) {
				for (int col = j * r_w; col < (j + 1) * r_w; col++) {
					if (image_add.at(row, col)[0] == 0 &&
						image_add.at(row, col)[1] == 0 &&
						image_add.at(row, col)[2] == 255) {
						// 查找符合要求的像素点
						Point midPoint;
						midPoint.x = col;
						midPoint.y = row;
						points.push_back(midPoint);
					}
				}
			}
			Rect rect = boundingRect(points);
			rectangle(image_add, rect, Scalar(0, 0, 0), 1, 8);
		}
	}
	namedWindow("add_mask", WINDOW_FREERATIO);
	imshow("add_mask", image_add);
}


void QuickDemo::histogram_local_image(Mat& image)
{
	Mat dst;
	Mat mask(image.rows, image.cols, CV_8UC3, Scalar(0, 0, 0));
	Rect rect = Rect(125, 1489, 309, 162);
	Scalar color = Scalar(255, 255, 255);
	rectangle(mask, rect, color, -1);
	Mat m_out;
	bitwise_and(image, mask, m_out);
	namedWindow("m_out", 0);
	imshow("m_out", m_out);
	Mat m_out_gray;
	cvtColor(m_out, m_out_gray, COLOR_BGR2GRAY);
	Mat AOI_hist = histogram_grayImage(m_out);
	namedWindow("AOI_hist", WINDOW_FREERATIO);
	imshow("AOI_hist", AOI_hist);

}

void QuickDemo::draw_interset(Mat &image_add)
{
	int h = image_add.rows; // 原图高
	int w = image_add.cols; // 原图宽
	vector points; // 设置容器，用来存储坐标
	for (int row = 0; row < h; row++) {
		for (int col = 0; col < w; col++) {
			if (image_add.at(row, col)[0] == 0 &&
				image_add.at(row, col)[1] == 0 &&
				image_add.at(row, col)[2] == 255) {
				// 查找符合要求的像素点
				Point midPoint;
				midPoint.x = col;
				midPoint.y = row;
				points.push_back(midPoint);
			}
		}
	}
	Rect rect = boundingRect(points);
	rectangle(image_add, rect, Scalar(0, 0, 255), 4, 8);
	namedWindow("add_mask", WINDOW_FREERATIO);
	imshow("add_mask", image_add);
}

void QuickDemo::image_mask(Mat& image, Mat& heightMat)
{
	Mat dst;
	cvtColor(image, dst, COLOR_BGR2GRAY);
	int h_height = heightMat.rows;
	int h_width = heightMat.cols;

	//mask
	for (int row = 0; row < h_height; row++)
	{
		for (int col = 0; col < h_width; col++)
		{
			if (heightMat.at(row, col) > 1000)//高度大于1000
			{
				image.at(row, col)[0] = 0;
				image.at(row, col)[1] = 0;
				image.at(row, col)[2] = 255;
			}
		}

	}
}

void QuickDemo::image_polar_mask_demo(Mat& image, Mat& heightMat)
{
	Mat dst;
	cvtColor(image, dst, COLOR_BGR2GRAY);
	int h_height = heightMat.rows;
	int h_width = heightMat.cols;

	//mask
	for (int row = 0; row < h_height; row++)
	{
		for (int col = 0; col < h_width; col++)
		{
			if (heightMat.at(row, col) >=1290 && heightMat.at(row, col) <=1400)//高度大于1000
			{
				image.at(row, col)[0] = 0;
				image.at(row, col)[1] = 0;
				image.at(row, col)[2] = 255;
			}
		}

	}
	
}

void QuickDemo::lead_box(Mat& image)
{
	//ROI区域主边框
	int h = image.rows; // 原图高
	int w = image.cols; // 原图宽
	vector points; // 设置容器，用来存储坐标
	for (int row = 0; row < h; row++) {
		for (int col = 0; col < w; col++) {
			if (image.at(row, col)[0] >= 100 &&
				image.at(row, col)[1] >= 150 &&
				image.at(row, col)[2] >= 50) {
				// 查找符合要求的像素点
				Point midPoint;
				midPoint.x = col;
				midPoint.y = row;
				points.push_back(midPoint);
			}
		}
	}
	Rect rect = boundingRect(points);
	rectangle(image, rect, Scalar(0, 0, 255), 1, 1);
	namedWindow("add_mask", WINDOW_AUTOSIZE);
	imshow("add_mask", image);

}

// 打掩模
Mat QuickDemo::lead_mask(Mat& image,Mat &gray_image)
{
	int height = image.rows;
	int width = image.cols;
	Mat dstImg = image.clone();// 新图像(寄存图像，输出就是这张图像)
	for (int i = 0; i < height; i++)
	{
		for (int j = 0; j < width; j++)
		{
			if (image.at(i, j)[0] < 150
				&& image.at(i, j)[1] < 133 
				&& image.at(i, j)[2] < 150)
			{
				dstImg.at(i, j)[0] = 245;
				dstImg.at(i, j)[1] = 4;
				dstImg.at(i, j)[2] = 251;
			}
		}
	}
	namedWindow("lead_mask", WINDOW_AUTOSIZE);
	imshow("lead_mask", dstImg);
	return dstImg;
}

Mat QuickDemo::sub_lead_mask(Mat& image, Mat& gray_image)
{
	int height = gray_image.rows;
	int width = gray_image.cols;
	int threshold_value = 100;
	int threshold_max = 255;
	vector points;
	for (int row = 0; row < height; row++)
	{
		for (int col = 0; col < width; col++)
		{
			if (image.at(row, col)[0] > 20 
				&& image.at(row, col)[1] > 20 
				&& image.at(row, col)[2] > 20
				&& gray_image.at(row, col) > 20)
			{
				image.at(row, col)[0] = 0;
				image.at(row, col)[1] = 0;
				image.at(row, col)[2] = 0;
			}
		}
	}
	//imshow("output", image);
	return image;

}

		
Mat QuickDemo::min_box(Mat& rgb_image, Mat& after_mask)
{
	Mat src_g;
	cvtColor(rgb_image, src_g, COLOR_BGR2GRAY);

	Mat dstImg = after_mask.clone();

	Mat binary_src;
	threshold(src_g, binary_src, 80, 255, THRESH_BINARY);

	vector> contours;
	vector hierarchy;
	findContours(binary_src, contours, hierarchy,
		RETR_EXTERNAL, CHAIN_APPROX_NONE);

	vector boundRect(contours.size());  //定义外接矩形集合
	Point2f rect[4];
	for (int i = 0; i < contours.size(); i++)
	{
		boundRect[i] = boundingRect(Mat(contours[i]));
		rectangle(dstImg, Point(boundRect[i].x, boundRect[i].y), 
			Point(boundRect[i].x + boundRect[i].width,
				boundRect[i].y + boundRect[i].height), Scalar(0, 255, 0), 2, 8);
	}
	return dstImg;
}


Mat QuickDemo::unevenLightCompensate(Mat& image, int blockSize)
{
	if (image.channels() == 3) cvtColor(image, image, 7);
	double average = mean(image)[0];
	int rows_new = ceil(double(image.rows) / double(blockSize));
	int cols_new = ceil(double(image.cols) / double(blockSize));
	Mat blockImage;
	blockImage = Mat::zeros(rows_new, cols_new, CV_32FC1);
	for (int i = 0; i < rows_new; i++)
	{
		for (int j = 0; j < cols_new; j++)
		{
			int rowmin = i * blockSize;
			int rowmax = (i + 1) * blockSize;
			if (rowmax > image.rows) rowmax = image.rows;
			int colmin = j * blockSize;
			int colmax = (j + 1) * blockSize;
			if (colmax > image.cols) colmax = image.cols;
			Mat imageROI = image(Range(rowmin, rowmax), Range(colmin, colmax));
			double temaver = mean(imageROI)[0];
			blockImage.at(i, j) = temaver;
		}
	}
	blockImage = blockImage - average;
	Mat blockImage2;
	resize(blockImage, blockImage2, image.size(), (0, 0), (0, 0), INTER_CUBIC);
	Mat image2;
	image.convertTo(image2, CV_32FC1);
	Mat dst = image2 - blockImage2;
	dst.convertTo(image, CV_8UC1);
	return image;
}

Mat QuickDemo::binary_image(Mat& image)
{
	Mat dst;
	threshold(image,dst,170,255,THRESH_BINARY);
	return dst;
}

test.cpp

#include
#include
#include
#include
using namespace cv;
using namespace std;


int main(int argc, char** argv) {
	Mat src = imread("D:\\testImage\\图片\\图片\\882019100041-4.jpg");
	if (src.empty()) {
		printf("could not load image...");
		return -1;
	}

	namedWindow("原图：", WINDOW_AUTOSIZE);
	imshow("原图：", src);

	QuickDemo qd;

	Mat photometric_processing_img = qd.unevenLightCompensate(src, 64);

	namedWindow("光度处理后图像：", WINDOW_AUTOSIZE);
	imshow("光度处理后图像：", photometric_processing_img);

	Mat binary_image = qd.binary_image(photometric_processing_img);
	namedWindow("二值化后图像：", WINDOW_AUTOSIZE);
	imshow("二值化后图像：", binary_image);


	waitKey(0);
	return 0;
}

//Mat roi_select(Mat& image, Point sp, Point ep) {
//	int dx = ep.x - sp.x;
//	int dy = ep.y - sp.y;
//	Rect box(sp.x, sp.y, dx, dy);//ROI区域大小
//	Mat new_img = image(box);
//	/*QuickDemo qd;
//	Mat ROI_his = qd.histogram_grayImage(new_img);
//	namedWindow("ROI_his", WINDOW_FREERATIO);
//	imshow("ROI_his", ROI_his);*/
//	return new_img;
//}
//
//
//
//int main()
//{
//	Mat src = imread("C:\\Users\\Administrator\\Documents\\WXWork\\1688850162194623\\Cache\\File\\2021-08\\0015.bmp");
//	if (src.empty())
//	{
//		printf("could not load image...");
//		return -1;
//	}
//	namedWindow("input_image", WINDOW_FREERATIO);
//	imshow("input_image", src);
//	int height = src.rows;
//	int width = src.cols;
//	cv::Mat textureMat(height, width, CV_8UC3);
//	std::ifstream("C:\\Users\\Administrator\\Documents\\WXWork\\1688850162194623\\Cache\\File\\2021 - 08\\0015.bmp", 
//		std::ios::in | std::ios::binary).read((char*)textureMat.data,width * height * 3 * sizeof(uchar));
//	cv::Mat heightMat(height, width, CV_32FC1);
//	std::ifstream("C:\\Users\\Administrator\\Documents\\WXWork\\1688850162194623\\Cache\\File\\2021-08\\0015_13", 
//		std::ios::in | std::ios::binary).read((char*)heightMat.data, width * height * sizeof(float));
//
//	QuickDemo qd;
//	Mat hist = qd.histogram_grayImage(heightMat);
//	namedWindow("hist", WINDOW_FREERATIO);
//	imshow("hist", hist);
//	
//	//left_top
//	Mat new_heightMat_lefttop = roi_select(heightMat, Point(666, 1706), Point(785, 1777));
//	Mat new_src_lefttop = roi_select(src, Point(666, 1706), Point(785, 1777));
//
//	//left_bottom
//	Mat new_heightMat_leftbot = roi_select(heightMat, Point(666, 2926), Point(785, 2997));
//	Mat new_src_leftbot = roi_select(src, Point(666, 2926), Point(785, 2997));
//
//	//right_bottom
//	Mat new_heightMat_rightbot = roi_select(heightMat, Point(1911, 2926), Point(2030, 2997));
//	Mat new_src_rightbot = roi_select(src, Point(1911, 2926), Point(2030, 2997));
//
//	//right_top
//	Mat new_heightMat_righttop = roi_select(heightMat, Point(1911, 1706), Point(2030, 1777));
//	Mat new_src_righttop = roi_select(src, Point(1911, 1706), Point(2030, 1777));
//
//	//掩模
//	qd.image_mask(new_src_lefttop, new_heightMat_lefttop);
//
//	qd.image_mask(new_src_leftbot, new_heightMat_leftbot);
//	qd.image_mask(new_src_righttop, new_heightMat_righttop);
//	qd.image_mask(new_src_rightbot, new_heightMat_rightbot);
//	qd.image_mask(new_src_rightbot, new_heightMat_rightbot);
//
//	namedWindow("形态学操作前", WINDOW_FREERATIO);
//	imshow("形态学操作前", src);
//	qd.elementsizechange_close_demo(src);
//	qd.draw_interset(src);

	/*Mat polar_heightMat = roi_select(heightMat, Point(1767, 2781), Point(1905, 2911));
	Mat polar_src = roi_select(src, Point(1767, 2781), Point(1905, 2911));
	qd.image_polar_mask_demo(polar_src, polar_heightMat);
	qd.elementsizechange_close_demo(src);
	float src_height = src.rows;
	float src_width = src.cols;
	circle(polar_src, Point(src_height / 2, src_width / 2), 50, Scalar(0, 0, 255));
	namedWindow("circle_mask_polar", WINDOW_FREERATIO);
	imshow("circle_mask_polar", src);*/

//	waitKey(0);
//	return 0;
//}




//int main() {
//	Mat src = imread("C:\\Users\\Administrator\\Documents\\WXWork\\1688850162194623\\Cache\\File\\2021-08\\0015.bmp");
//	if (src.empty())
//	{
//		printf("could not load image...");
//		return -1;
//	}
//	//namedWindow("input_image", WINDOW_FREERATIO);
//	//imshow("input_image", src);
//	Mat roi_src = roi_select(src, Point(672, 1571), Point(2007, 1724));
//	namedWindow("roi_src", WINDOW_AUTOSIZE);
//	imshow("roi_src", roi_src);
//
//
//	Mat gray_roi_src;
//	cvtColor(roi_src, gray_roi_src, COLOR_BGR2GRAY);
//	imshow("gray_roi_src", gray_roi_src);
//
//	QuickDemo qd;
//
//	//思想是；把要处理的roi区域复制为两个图像，一个用来处理打掩模，以及满足一定的条件做一些操作，
//	//然后在把他加到原有没有处理的ROI图像上。
//
//	//引脚根部搜索:通过亮度和颜色的设定标示出引脚本体部分
//	Mat mask_img = qd.lead_mask(roi_src, gray_roi_src);
//
//	//通过亮度和颜色的设定标示出引脚部分
//	Mat after_mask = qd.sub_lead_mask(mask_img, gray_roi_src);
//	imshow("after_mask", after_mask);
//
//	//通过亮度和颜色的设定标焊锡部分(引脚的头和尾)
//	Mat box_label = qd.min_box(roi_src, after_mask);
//	imshow("box_label", box_label);
//
//	waitKey(0);
//	return 0;
//}