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import cv2
from matplotlib import pyplot as plt
import numpy as np
from sklearn.cluster import *
from sklearn import mixture
from sklearn import metrics
from scipy.stats import mode

class ImgProcessor:

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template <typename Comparable>
int binarySearch(const vector<Comparable>& a, const Comparable& x) {
  int low = 0, high = a.size() - 1;

  while (low <= high) {
    int mid = (low + high) / 2;

    if (a[mid] < x) 
      low = mid + 1;
    else(a[mid] > x)

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/**
 * @param a
 * @return maxsum
 * @brief 最大相连子序列和的 O(n^3) 算法
 */
int maxSubSum1(const vector<int> &a) {
    int maxSum = 0;

    for (int i = 0; i < a.size(); ++i)
        for (int j = i; j < a.size(); ++j) {

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/**
 * @param a
 * @return maxsum
 * @brief 最大相连子序列和的 O(n^2) 算法
 */
int maxSubSum2(const vector<int> &a) {
    int maxSum = 0;

    for (int i = 0; i < a.size(); ++i) {
        int thisSum = 0;

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/**
 * @param a
 * @param left
 * @param right
 * @return maxSum between left and right
 * @brief 相连最大子序列和的递归算法
 * @note 找出生成 [left ... right] 的子数组的最大和，不试图保留具体的最佳序列
 */
int maxSumRec(const vector<int> &a, int left, int right) {
    if (left == right) // 基准情形

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long long gcd(long long m, long long n) {
    while (n != 0) {
        long long rem = m % n;
        m = n;
        n = rem;
    }
    return m;
}

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long long pow(long long x, int n) {
    if (n == 0)
        return 1;
    if (n == 1)
        return x;
    if (n & 0x1)
        return pow(x * x, n / 2) * x;
    else
        return pow(x * x, n / 2);
}

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template<typename Object>
class Vector {
private:
    int theSize;
    int theCapacity;
    Object *objects;

    static const int SPARE_CAPACITY = 16;

public:

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template<typename Object>
struct Node {
    Object data;
    Node *prev;
    Node *next;

    Node(const Object &d = Object{}, Node *p = nullptr, Node *n = nullptr)
            : data{d}, prev{p}, next{n} {}

    Node(Object &&d, Node *p = nullptr, Node *n = nullptr)