粒子群优化相机内参的python代码

粒子群优化是一种用于最优化复杂问题的算法。在相机内参优化中，你可以使用粒子群优化来寻找最佳的相机内参（即焦距、主点偏移、旋转矩阵、畸变参数等）。

这里是一个用 Python 实现粒子群优化的示例代码：

import numpy as np
import random
import math

def optimize(objective_function, bounds, population_size=100, num_iterations=100):
  # 初始化种群
  population = []
  for i in range(population_size):
    individual = []
    for j in range(len(bounds)):
      individual.append(random.uniform(bounds[j][0], bounds[j][1]))
    population.append(individual)

  # 迭代求解
  for t in range(num_iterations):
    # 计算种群中每个个体的适应度
    fitness = []
    for i in range(population_size):
      fitness.append(objective_function(population[i]))

    # 找到最优解
    best = np.argmax(fitness)
    if t % 10 == 0:
      print("Iteration", t, ": best solution is", population[best], "with fitness", fitness[best])

    # 更新粒子的速度和位置
    for i in range(population_size):
      # 计算粒子的速度
      velocity = []
      for j in range(len(bounds)):
        v_i = random.uniform(0, 1) * population[i][j] + random.uniform(0, 1) * population[best][j] - population[i][j]
        velocity.append(v_i)

      # 更新粒子的位置
      for j in range(len(bounds)):
        population[i][j] = population[i][j] + velocity[j]

        # 限制粒子的位置在可行解范围内
        if population[i][j] < bounds[j][0]:
          population[i][j] = bounds[j][0]
        elif population[i][j] > bounds[j][1]:
          population[i][j] = bounds[j][1]

# 测试优化函数
def test_optimize