opencv入门基础（c++）【五】

二十五、 轮廓发现(find contour in your image)

25.1轮廓发现(find contour)

轮廓发现是基于图像边缘提取的基础寻找对象轮廓的方法。
所以边缘提取的阈值选定会影响最终轮廓发现结果
API介绍
findContours发现轮廓
drawContours绘制轮廓

轮廓发现findContours

在二值图像上发现轮廓使用API cv::findContours(
InputOutputArray binImg, // 输入图像，非0的像素被看成1,0的像素值保持不变，8-bit
OutputArrayOfArrays contours,// 全部发现的轮廓对象
OutputArray, hierachy// 图该的拓扑结构，可选，该轮廓发现算法正是基于图像拓扑结构实现。
int mode, // 轮廓返回的模式
int method,// 发现方法
Point offset=Point()// 轮廓像素的位移，默认（0, 0）没有位移
)

轮廓绘制(draw contour)

在二值图像上发现轮廓使用API cv::findContours之后对发现的轮廓数据进行绘制显示
drawContours(
InputOutputArray binImg, // 输出图像
OutputArrayOfArrays contours,// 全部发现的轮廓对象
Int contourIdx// 轮廓索引号
const Scalar & color,// 绘制时候颜色
int thickness,// 绘制线宽
int lineType ,// 线的类型LINE_8
InputArray hierarchy,// 拓扑结构图
int maxlevel,// 最大层数， 0只绘制当前的，1表示绘制绘制当前及其内嵌的轮廓
Point offset=Point()// 轮廓位移，可选

步骤

输入图像转为灰度图像cvtColor
使用Canny进行边缘提取，得到二值图像
使用findContours寻找轮廓
使用drawContours绘制轮廓

#include 
#include 
#include 

using namespace std;
using namespace cv;

Mat src, dst;
const char* output_win = "findcontours-demo";
int threshold_value = 100;
int threshold_max = 255;
RNG rng;
void Demo_Contours(int, void*);
int main(int argc, char** argv) {
	src = imread("C:/Users/Admin/Desktop/1.jpg");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}
	namedWindow("input-image", WINDOW_AUTOSIZE);
	namedWindow(output_win, WINDOW_AUTOSIZE);
	imshow("input-image", src);
	cvtColor(src, src, COLOR_BGR2GRAY);

	const char* trackbar_title = "Threshold Value:";
	createTrackbar(trackbar_title, output_win, &threshold_value, threshold_max, Demo_Contours);
	Demo_Contours(0, 0);

	waitKey(0);
	return 0;
}

void Demo_Contours(int, void*) {
	Mat canny_output;
	vector<vector<Point>> contours;
	vector<Vec4i> hierachy;
	Canny(src, canny_output, threshold_value, threshold_value * 2, 3, false);
	findContours(canny_output, contours, hierachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));

	dst = Mat::zeros(src.size(), CV_8UC3);
	RNG rng(12345);
	for (size_t i = 0; i < contours.size(); i++) {
		Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
		drawContours(dst, contours, i, color, 2, 8, hierachy, 0, Point(0, 0));
	}
	imshow(output_win, dst);
}

二十六、凸包-Convex Hull

26.1什么是凸包(Convex Hull)

什么是凸包(Convex Hull)，在一个多变形边缘或者内部任 意两个点的连线都包含在多边形边界或者内部。
正式定义：
包含点集合S中所有点的最小凸多边形称为凸包

26.2概念介绍-Graham扫描算法

首先选择Y方向最低的点作为起始点p0
从p0开始极坐标扫描，依次添加p1….pn（排序顺序是根据极坐标的角度大小，逆时针方向）
对每个点pi来说，如果添加pi点到凸包中导致一个左转向（逆时针方法）则添加该点到凸包， 反之如果导致一个右转向（顺时针方向）删除该点从凸包中

26.3API说明cv::convexHull

convexHull(
InputArray points,// 输入候选点，来自findContours
OutputArray hull,// 凸包
bool clockwise,// default true, 顺时针方向
bool returnPoints）// true 表示返回点个数，如果第二个参数是 vector则自动忽略

26.4实现步骤

首先把图像从RGB转为灰度
然后再转为二值图像
在通过发现轮廓得到候选点
凸包API调用
绘制显示。

#include 
#include 
#include 

using namespace std;
using namespace cv;
Mat src, src_gray, dst;
int threshold_value = 100;
int threshold_max = 255;
const char* output_win = "convex hull demo";
void Threshold_Callback(int, void*);
RNG rng(12345);
int main(int argc, char** argv) {
	src = imread("C:/Users/Admin/Desktop/1.jpg");
	if (!src.data) {
		printf("could not load image...\n");
		return -1;
	}
	const char* input_win = "input image";
	namedWindow(input_win, WINDOW_AUTOSIZE);
	namedWindow(output_win, WINDOW_NORMAL);
	const char* trackbar_label = "Threshold : ";

	cvtColor(src, src_gray, COLOR_BGR2GRAY);
	blur(src_gray, src_gray, Size(3, 3), Point(-1, -1), BORDER_DEFAULT);
	imshow(input_win, src_gray);

	createTrackbar(trackbar_label, output_win, &threshold_value, threshold_max, Threshold_Callback);
	Threshold_Callback(0, 0);
	waitKey(0);
	return 0;
}

void Threshold_Callback(int, void*) {
	Mat bin_output;
	vector<vector<Point>> contours;
	vector<Vec4i> hierachy;

	threshold(src_gray, bin_output, threshold_value, threshold_max, THRESH_BINARY);
	findContours(bin_output, contours, hierachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));

	vector<vector<Point>> convexs(contours.size());
	for (size_t i = 0; i < contours.size(); i++) {
		convexHull(contours[i], convexs[i], false, true);
	}

	// 绘制
	dst = Mat::zeros(src.size(), CV_8UC3);
	vector<Vec4i> empty(0);
	for (size_t k = 0; k < contours.size(); k++) {
		Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
		drawContours(dst, contours, k, color, 2, LINE_8, hierachy, 0, Point(0, 0));
		drawContours(dst, convexs, k, color, 2, LINE_8, empty, 0, Point(0, 0));
	}
	imshow(output_win, dst);

	return;
}

二十七、轮廓周围绘制矩形框和圆形框

27.1轮廓周围绘制矩形 -API

approxPolyDP(InputArray curve, OutputArray approxCurve, double epsilon, bool closed)

基于RDP算法实现,目的是减少多边形轮廓点数

cv::boundingRect(InputArray points)得到轮廓周围最小矩形左上交点坐标和右下角点坐标，绘制一个矩形
cv::minAreaRect(InputArray points)得到一个旋转的矩形，返回旋转矩形

27.2轮廓周围绘制圆和椭圆-API

cv::minEnclosingCircle(InputArray points, //得到最小区域圆形
Point2f& center, // 圆心位置
float& radius)// 圆的半径
cv::fitEllipse(InputArray points)得到最小椭圆

27.3 实现步骤

首先将图像变为二值图像
发现轮廓，找到图像轮廓
通过相关API在轮廓点上找到最小包含矩形和圆，旋转矩形与椭圆。
绘制它们。

#include 
#include 
#include 

using namespace std;
using namespace cv;
Mat src, gray_src, drawImg;
int threshold_v = 170;
int threshold_max = 255;
const char* output_win = "rectangle-demo";
RNG rng(12345);
void Contours_Callback(int, void*);
int main(int argc, char** argv) {
	src = imread("C:/Users/Admin/Desktop/1.jpg");

	if (!src.data) {
		printf("could not load image...\n");
		return -1;
	}
	cvtColor(src, gray_src, COLOR_BGR2GRAY);
	blur(gray_src, gray_src, Size(3, 3), Point(-1, -1));

	const char* source_win = "input image";
	namedWindow(source_win, WINDOW_AUTOSIZE);
	namedWindow(output_win, WINDOW_AUTOSIZE);
	imshow(source_win, src);

	createTrackbar("Threshold Value:", output_win, &threshold_v, threshold_max, Contours_Callback);
	Contours_Callback(0, 0);

	waitKey(0);
	return 0;
}

void Contours_Callback(int, void*) {
	Mat binary_output;
	vector<vector<Point>> contours;
	vector<Vec4i> hierachy;
	threshold(gray_src, binary_output, threshold_v, threshold_max, THRESH_BINARY);
	//imshow("binary image", binary_output);
	findContours(binary_output, contours, hierachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(-1, -1));

	vector<vector<Point>> contours_ploy(contours.size());
	vector<Rect> ploy_rects(contours.size());
	vector<Point2f> ccs(contours.size());
	vector<float> radius(contours.size());

	vector<RotatedRect> minRects(contours.size());
	vector<RotatedRect> myellipse(contours.size());

	for (size_t i = 0; i < contours.size(); i++) {
		approxPolyDP(Mat(contours[i]), contours_ploy[i], 3, true);
		ploy_rects[i] = boundingRect(contours_ploy[i]);
		minEnclosingCircle(contours_ploy[i], ccs[i], radius[i]);
		if (contours_ploy[i].size() > 5) {
			myellipse[i] = fitEllipse(contours_ploy[i]);
			minRects[i] = minAreaRect(contours_ploy[i]);
		}
	}

	// draw it
	drawImg = Mat::zeros(src.size(), src.type());
	Point2f pts[4];
	for (size_t t = 0; t < contours.size(); t++) {
		Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
		//rectangle(drawImg, ploy_rects[t], color, 2, 8);
		//circle(drawImg, ccs[t], radius[t], color, 2, 8);
		if (contours_ploy[t].size() > 5) {
			ellipse(drawImg, myellipse[t], color, 1, 8);
			minRects[t].points(pts);
			for (int r = 0; r < 4; r++) {
				line(drawImg, pts[r], pts[(r + 1) % 4], color, 1, 8);
			}
		}
	}

	imshow(output_win, drawImg);
	return;
}

二十八、图像矩(Image Moments)

28.1矩的概念介绍

28.2API介绍与使用 – cv::moments 计算生成数据

moments(
InputArray array,//输入数据
bool binaryImage=false // 是否为二值图像
)

contourArea(
InputArray contour,//输入轮廓数据
bool oriented// 默认false、返回绝对值)

arcLength(
InputArray curve,//输入曲线数据
bool closed// 是否是封闭曲线)

28.3实现步骤

提取图像边缘
发现轮廓
计算每个轮廓对象的矩
计算每个对象的中心、弧长、面积

#include 
#include 
#include 

using namespace std;
using namespace cv;

Mat src, gray_src;
int threshold_value = 80;
int threshold_max = 255;
const char* output_win = "image moents demo";
RNG rng(12345);
void Demo_Moments(int, void*);
int main(int argc, char** argv) {
	src = imread("C:/Users/Admin/Desktop/1.jpg");
	if (!src.data) {
		printf("could not load image...\n");
		return -1;
	}
	cvtColor(src, gray_src, COLOR_BGR2GRAY);
	GaussianBlur(gray_src, gray_src, Size(3, 3), 0, 0);

	char input_win[] = "input image";
	namedWindow(input_win, WINDOW_AUTOSIZE);
	namedWindow(output_win, WINDOW_AUTOSIZE);
	imshow(input_win, src);

	createTrackbar("Threshold Value : ", output_win, &threshold_value, threshold_max, Demo_Moments);
	Demo_Moments(0, 0);

	waitKey(0);
	return 0;
}

void Demo_Moments(int, void*) {
	Mat canny_output;
	vector<vector<Point>> contours;
	vector<Vec4i> hierachy;

	Canny(gray_src, canny_output, threshold_value, threshold_value * 2, 3, false);
	findContours(canny_output, contours, hierachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));

	vector<Moments> contours_moments(contours.size());
	vector<Point2f> ccs(contours.size());
	for (size_t i = 0; i < contours.size(); i++) {
		contours_moments[i] = moments(contours[i]);
		ccs[i] = Point(static_cast<float>(contours_moments[i].m10 / contours_moments[i].m00), static_cast<float>(contours_moments[i].m01 / contours_moments[i].m00));
	}

	Mat drawImg;// = Mat::zeros(src.size(), CV_8UC3);
	src.copyTo(drawImg);
	for (size_t i = 0; i < contours.size(); i++) {
		if (contours[i].size() < 100) {
			continue;
		}
		Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
		printf("center point x : %.2f y : %.2f\n", ccs[i].x, ccs[i].y);
		printf("contours %d area : %.2f   arc length : %.2f\n", i, contourArea(contours[i]), arcLength(contours[i], true));
		drawContours(drawImg, contours, i, color, 2, 8, hierachy, 0, Point(0, 0));
		circle(drawImg, ccs[i], 2, color, 2, 8);
	}

	imshow(output_win, drawImg);
	return;
}

二十九、点多边形测试

29.1概念介绍 - 点多边形测试

测试一个点是否在给定的多边形内部，边缘或者外部

29.2API介绍 cv::pointPolygonTest

pointPolygonTest(
InputArray contour,// 输入的轮廓
Point2f pt, // 测试点
bool measureDist // 是否返回距离值，如果是false，1表示在内面，0表示在边界上，-1表示在外部，true返回实际距离
)

返回数据是double类型

29.3实现步骤

构建一张400x400大小的图片， Mat::Zero(400, 400, CV_8UC1)
画上一个六边形的闭合区域line
发现轮廓
对图像中所有像素点做点 多边形测试，得到距离，归一化后显示。

#include 
#include 
#include 

using namespace std;
using namespace cv;
int main(int argc, char** argv) {
	const int r = 100;
	Mat src = Mat::zeros(r * 4, r * 4, CV_8UC1);

	vector<Point2f> vert(6);
	vert[0] = Point(3 * r / 2, static_cast<int>(1.34*r));
	vert[1] = Point(1 * r, 2 * r);
	vert[2] = Point(3 * r / 2, static_cast<int>(2.866*r));
	vert[3] = Point(5 * r / 2, static_cast<int>(2.866*r));
	vert[4] = Point(3 * r, 2 * r);
	vert[5] = Point(5 * r / 2, static_cast<int>(1.34*r));

	for (int i = 0; i < 6; i++) {
		line(src, vert[i], vert[(i + 1) % 6], Scalar(255), 3, 8, 0);
	}

	vector<vector<Point>> contours;
	vector<Vec4i> hierachy;
	Mat csrc;
	src.copyTo(csrc);
	findContours(csrc, contours, hierachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));
	Mat raw_dist = Mat::zeros(csrc.size(), CV_32FC1);
	for (int row = 0; row < raw_dist.rows; row++) {
		for (int col = 0; col < raw_dist.cols; col++) {
			double dist = pointPolygonTest(contours[0], Point2f(static_cast<float>(col), static_cast<float>(row)), true);
			raw_dist.at<float>(row, col) = static_cast<float>(dist);
		}
	}

	double minValue, maxValue;
	minMaxLoc(raw_dist, &minValue, &maxValue, 0, 0, Mat());
	Mat drawImg = Mat::zeros(src.size(), CV_8UC3);
	for (int row = 0; row < drawImg.rows; row++) {
		for (int col = 0; col < drawImg.cols; col++) {
			float dist = raw_dist.at<float>(row, col);
			if (dist > 0) {
				drawImg.at<Vec3b>(row, col)[0] = (uchar)(abs(1.0 - (dist / maxValue)) * 255);
			}
			else if (dist < 0) {
				drawImg.at<Vec3b>(row, col)[2] = (uchar)(abs(1.0 - (dist / minValue)) * 255);
			}
			else {
				drawImg.at<Vec3b>(row, col)[0] = (uchar)(abs(255 - dist));
				drawImg.at<Vec3b>(row, col)[1] = (uchar)(abs(255 - dist));
				drawImg.at<Vec3b>(row, col)[2] = (uchar)(abs(255 - dist));
			}
		}
	}

	const char* output_win = "point polygon test demo";
	char input_win[] = "input image";
	namedWindow(input_win, WINDOW_AUTOSIZE);
	namedWindow(output_win, WINDOW_AUTOSIZE);

	imshow(input_win, src);
	imshow(output_win, drawImg);

	waitKey(0);
	return 0;
}