double thresh = Cv2.Threshold(roiMat, new Mat(), 0, 255, ThresholdTypes.Binary | (isJudgeByGreatThan ? ThresholdTypes.Otsu : ThresholdTypes.OtsuInv));中opencvsharp4.6中没有 ThresholdTypes.OtsuInv这个方法请用其它方法实现，编写新代码

好的，如果你想使用类似于OtsuInv的阈值类型，你可以考虑使用反相图像来实现。具体来说，可以先使用Otsu方法计算出一个阈值，然后将图像进行反相处理，再利用这个阈值进行二值化，这样就相当于使用了OtsuInv的阈值类型。下面是一个简单的示例代码：

Mat grayImage = new Mat();
Cv2.CvtColor(srcImage, grayImage, ColorConversionCodes.BGR2GRAY);

Mat binaryImage = new Mat();
double otsuThreshold = Cv2.Threshold(grayImage, binaryImage, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
Cv2.BitwiseNot(grayImage, grayImage);
Cv2.Threshold(grayImage, binaryImage, otsuThreshold, 255, ThresholdTypes.Binary);

// binaryImage即为处理后的二值图像

在这段代码中，首先将原始图像转换为灰度图像，然后使用Otsu方法计算出一个阈值，并将图像进行反相处理。最后再利用这个阈值进行二值化，得到处理后的二值图像。

相关问题

import numpy as np import cv2 as cv # 加载图片 img = cv.imread('4.jpg') # 灰度化 img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) # 二值化 ret, thresh = cv.threshold(img_gray, 127, 255, cv.THRESH_BINARY) # 寻找连通域 num_labels, labels, stats, centroids = cv.connectedComponentsWithStats(thresh, connectivity=8) # 计算平均面积 areas = list() for i in range(num_labels): areas.append(stats[i][-1]) print("轮廓%d的面积:%d" % (i, stats[i][-1])) area_avg = np.average(areas[1:-1]) print("轮廓平均面积:", area_avg) # 筛选超过平均面积的连通域 image_filtered = np.zeros_like(img) for (i, label) in enumerate(np.unique(labels)): # 如果是背景，忽略 if label == 0: continue if stats[i][-1] < area_avg : image_filtered[labels == i] = 255 #cv.imshow("image_filtered", image_filtered) #cv.imshow("img", img) cv.imwrite('4.jpg',image_filtered ) cv.waitKey() cv.destroyAllWindows(),将上述代码转化为c++形式

以下是将上述Python代码转化为C++形式的示例：

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

using namespace std;
using namespace cv;

int main() {
    // 加载图片
    Mat img = imread("4.jpg");

    // 灰度化
    Mat img_gray;
    cvtColor(img, img_gray, COLOR_BGR2GRAY);

    // 二值化
    Mat thresh;
    threshold(img_gray, thresh, 127, 255, THRESH_BINARY);

    // 寻找连通域
    Mat labels, stats, centroids;
    int num_labels = connectedComponentsWithStats(thresh, labels, stats, centroids, 8);

    // 计算平均面积
    vector<int> areas;
    for (int i = 0; i < num_labels; i++) {
        areas.push_back(stats.at<int>(i, CC_STAT_AREA));
        cout << "轮廓" << i << "的面积：" << stats.at<int>(i, CC_STAT_AREA) << endl;
    }
    double area_avg = mean(Mat(areas))[0];
    cout << "轮廓平均面积：" << area_avg << endl;

    // 筛选超过平均面积的连通域
    Mat image_filtered = Mat::zeros(img.size(), CV_8UC1);
    for (int i = 1; i < num_labels; i++) {
        if (stats.at<int>(i, CC_STAT_AREA) < area_avg) {
            labels.setTo(0, labels == i);
        } else {
            image_filtered.setTo(255, labels == i);
        }
    }

    // 显示结果
    imshow("image_filtered", image_filtered);
    imshow("img", img);
    waitKey();
    destroyAllWindows();

    // 保存结果
    imwrite("4.jpg", image_filtered);

    return 0;
}

需要注意的是，C++中没有Python中的`list`类型，需要使用`vector`替代。同时，C++中没有Python中的`np.average()`方法，需要使用OpenCV的`mean()`方法计算平均值。此外，C++中的Mat类需要使用`.at()`方法访问元素，而不是Python中的下标索引。

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;
}