Point *convex_hull(Point *points, int n, int *m)参数分别是什么

参数说明如下：

- `points`：点的数组，每个点是一个二维坐标点。类型为 `Point*`。
- `n`：点的个数，即数组 `points` 的长度。类型为 `int`。
- `m`：返回凸包上点的个数，即凸包的顶点数。类型为 `int*`，需要传入地址，函数内部会修改该值。

函数的返回值是一个 `Point*` 类型的数组，表示凸包上的点。

相关问题

import cv2 import math def cal_ang(start, center, end): point_1 = start point_2 = center point_3 = end a = math.sqrt( (point_2[0] - point_3[0]) * (point_2[0] - point_3[0]) + (point_2[1] - point_3[1]) * (point_2[1] - point_3[1])) b = math.sqrt( (point_1[0] - point_3[0]) * (point_1[0] - point_3[0]) + (point_1[1] - point_3[1]) * (point_1[1] - point_3[1])) c = math.sqrt( (point_1[0] - point_2[0]) * (point_1[0] - point_2[0]) + (point_1[1] - point_2[1]) * (point_1[1] - point_2[1])) A = math.degrees(math.acos((a * a - b * b - c * c) / (-2 * b * c))) B = math.degrees(math.acos((b * b - a * a - c * c) / (-2 * a * c))) C = math.degrees(math.acos((c * c - a * a - b * b) / (-2 * a * b))) return B img = cv2.imread('46.png') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret,thresh = cv2.threshold(gray, 70, 255, cv2.THRESH_BINARY) contours,hierarchy=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) hull = cv2.convexHull(contours[0],returnPoints=False) defects = cv2.convexityDefects(contours[0],hull) start = end = (0,0) for i in range(0,defects.shape[0]): s,e,f,d = defects[i,0] start = tuple(contours[0][s][0]) end = tuple(contours[0][e][0]) far = tuple(contours[0][f][0]) if d > 5000: cv2.line(img,start,end,[0,255,0],2) cv2.circle(img,end,5,[0,0,255],-1) cv2.circle(img,start,5,[0,0,255],-1) break cv2.imshow('find', img) center,radius = cv2.minEnclosingCircle(contours[0]) cv2.circle(img,(int(center[0]),int(center[1])),8,(255,0,255),-1) cv2.circle(img,end,8,[255,0,0],-1) cv2.circle(img,start,8,[255,0,0],-1) cv2.line(img,start,(int(center[0]),int(center[1])),[0,0,255],2) cv2.line(img,end,(int(center[0]),int(center[1])),[0,0,255],2) angle = cal_ang(start,center,end) print('angle = %0.2f' % angle) length = (1 - angle / 360.0) * math.pi * radius * 2 print((angle / 360.0)) print('radius = %0.2f' % radius) strL = 'length=%0.2f' % length cv2.putText(img,strL,(int(center[0]-40),int(center[1]+40)),0,0.8,(0,255,0),2) cv2.imshow('result', img) angle_1 = cal_ang(start, center, ((center[0]+100),(center[1]))) angle_2 = cal_ang(end, center, ((center[0]+100),(center[1]))) cv2.ellipse(img,(int(center[0]),int(center[1])),(int(radius),int(radius)),0,-angle_1,0,(255,0,255),2, cv2.LINE_AA) cv2.ellipse(img,(int(center[0]),int(center[1])),(int(radius),int(radius)),0,0,angle_2,(255,0,255),2,cv2.LINE_AA) cv2.imshow('result', img) cv2.imwrite('result.png',img) cv2.waitKey(0) cv2.destroyAllWindows(),将这段代码转换为c++

以下是将Python代码转换为C++的结果。注意，由于数据类型和库函数的区别，代码可能需要进行微调才能正常运行。

#include <iostream>
#include <opencv2/opencv.hpp>
#include <math.h>

using namespace std;
using namespace cv;

double cal_ang(Point start, Point center, Point end) {
    Point2f point_1 = start;
    Point2f point_2 = center;
    Point2f point_3 = end;
    double a = sqrt(pow(point_2.x - point_3.x, 2) + pow(point_2.y - point_3.y, 2));
    double b = sqrt(pow(point_1.x - point_3.x, 2) + pow(point_1.y - point_3.y, 2));
    double c = sqrt(pow(point_1.x - point_2.x, 2) + pow(point_1.y - point_2.y, 2));
    double A = acos((a * a - b * b - c * c) / (-2 * b * c)) * 180 / CV_PI;
    double B = acos((b * b - a * a - c * c) / (-2 * a * c)) * 180 / CV_PI;
    double C = acos((c * c - a * a - b * b) / (-2 * a * b)) * 180 / CV_PI;
    return B;
}

int main() {
    Mat img = imread("46.png");
    Mat gray;
    cvtColor(img, gray, COLOR_BGR2GRAY);
    threshold(gray, gray, 70, 255, THRESH_BINARY);
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    findContours(gray, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_NONE);
    vector<vector<Point>> hull(contours.size());
    vector<vector<int>> hullsI(contours.size());
    vector<vector<Vec4i>> defects(contours.size());
    convexHull(contours[0], hull[0], false);
    convexHull(contours[0], hullsI[0], false);
    if (hullsI[0].size() > 0) {
        Point2f* pts = new Point2f[hullsI[0].size()];
        for (size_t i = 0; i < hullsI[0].size(); i++) {
            pts[i] = contours[0][hullsI[0][i]];
        }
        int n = hullsI[0].size();
        convexityDefects(pts, n, hullsI[0], defects[0]);
        delete[] pts;
    }
    Point start, end;
    for (int i = 0; i < defects[0].size(); i++) {
        Vec4i& v = defects[0][i];
        int startidx = v[0];
        Point ptStart(contours[0][startidx]);
        int endidx = v[1];
        Point ptEnd(contours[0][endidx]);
        int faridx = v[2];
        Point ptFar(contours[0][faridx]);
        if (v[3] > 5000) {
            line(img, ptStart, ptEnd, Scalar(0, 255, 0), 2);
            circle(img, ptEnd, 5, Scalar(0, 0, 255), -1);
            circle(img, ptStart, 5, Scalar(0, 0, 255), -1);
            start = ptStart;
            end = ptEnd;
            break;
        }
    }
    Point2f center;
    float radius = 0;
    minEnclosingCircle(contours[0], center, radius);
    circle(img, center, 8, Scalar(255, 0, 255), -1);
    circle(img, end, 8, Scalar(255, 0, 0), -1);
    circle(img, start, 8, Scalar(255, 0, 0), -1);
    line(img, start, Point(center), Scalar(0, 0, 255), 2);
    line(img, end, Point(center), Scalar(0, 0, 255), 2);
    double angle = cal_ang(start, center, end);
    cout << "angle = " << angle << endl;
    double length = (1 - angle / 360.0) * CV_PI * radius * 2;
    cout << angle / 360.0 << endl;
    cout << "radius = " << radius << endl;
    stringstream ss;
    ss << "length=" << fixed << setprecision(2) << length;
    String strL = ss.str();
    putText(img, strL, Point(center.x - 40, center.y + 40), 0, 0.8, Scalar(0, 255, 0), 2);
    double angle_1 = cal_ang(start, center, Point(center.x + 100, center.y));
    double angle_2 = cal_ang(end, center, Point(center.x + 100, center.y));
    ellipse(img, center, Size(radius, radius), 0, -angle_1, 0, Scalar(255, 0, 255), 2, LINE_AA);
    ellipse(img, center, Size(radius, radius), 0, 0, angle_2, Scalar(255, 0, 255), 2, LINE_AA);
    imshow("result", img);
    imwrite("result.png", img);
    waitKey(0);
    destroyAllWindows();
    return 0;
}

C语言用蛮力法解决凸包问题

凸包问题可以使用蛮力法来解决，其基本思想是考虑将所有点都进行组合，找到一个能够包含所有点的凸多边形。

具体步骤如下：

1. 枚举所有点对，找到其中的一条直线分割点集为两个部分，每个部分内部都不包含其他点。

2. 对于两个部分，分别递归地执行第1步，直到只剩下一个点或者没有点。

3. 最后将所有的凸多边形合并成一个凸包。

下面是一个使用蛮力法求解凸包的C语言实现代码示例：

#include <stdio.h>

struct Point {
    int x;
    int y;
};

int cross_product(struct Point a, struct Point b, struct Point c) {
    return (b.x - a.x) * (c.y - a.y) - (b.y - a.y) * (c.x - a.x);
}

int is_left_turn(struct Point a, struct Point b, struct Point c) {
    return cross_product(a, b, c) > 0;
}

int is_right_turn(struct Point a, struct Point b, struct Point c) {
    return cross_product(a, b, c) < 0;
}

void find_upper_hull(struct Point *points, int n, struct Point *hull, int *hull_size) {
    hull[0] = points[0];
    hull[1] = points[1];
    *hull_size = 2;

    for (int i = 2; i < n; i++) {
        while (*hull_size > 1 && is_right_turn(hull[*hull_size - 2], hull[*hull_size - 1], points[i])) {
            *hull_size -= 1;
        }
        hull[*hull_size] = points[i];
        *hull_size += 1;
    }
}

void find_lower_hull(struct Point *points, int n, struct Point *hull, int *hull_size) {
    hull[0] = points[n - 1];
    hull[1] = points[n - 2];
    *hull_size = 2;

    for (int i = n - 3; i >= 0; i--) {
        while (*hull_size > 1 && is_right_turn(hull[*hull_size - 2], hull[*hull_size - 1], points[i])) {
            *hull_size -= 1;
        }
        hull[*hull_size] = points[i];
        *hull_size += 1;
    }
}

void find_convex_hull(struct Point *points, int n, struct Point *hull, int *hull_size) {
    struct Point upper_hull[n];
    struct Point lower_hull[n];

    find_upper_hull(points, n, upper_hull, hull_size);
    find_lower_hull(points, n, lower_hull, hull_size);

    for (int i = 0; i < *hull_size; i++) {
        hull[i] = upper_hull[i];
    }
    for (int i = 1; i < *hull_size - 1; i++) {
        hull[*hull_size + i - 1] = lower_hull[i];
    }
    *hull_size = 2 * (*hull_size - 1);
}

int main() {
    struct Point points[] = {{0, 3}, {1, 1}, {2, 2}, {4, 4}, {0, 0}, {1, 2}, {3, 1}, {3, 3}};
    int n = sizeof(points) / sizeof(points[0]);

    struct Point hull[n];
    int hull_size;

    find_convex_hull(points, n, hull, &hull_size);

    printf("Convex Hull:\n");
    for (int i = 0; i < hull_size; i++) {
        printf("(%d, %d)\n", hull[i].x, hull[i].y);
    }

    return 0;
}

该代码实现了一个简单的凸包求解算法，可以通过枚举所有点对，找到凸包的上下半部分，然后将它们合并成一个凸包。由于蛮力法的时间复杂度为O(n^3)，因此对于大规模的数据集，该算法可能不太适合使用。