粒子群优化的相机标定python代码
时间: 2023-08-17 07:06:15 浏览: 31
以下是使用粒子群优化进行相机标定的Python代码示例:
``` python
import numpy as np
import cv2
import math
import random
def load_data():
data = np.loadtxt('data.txt')
return data
def save_data(data):
np.savetxt('data.txt', data)
def generate_initial_particles(particle_count):
particles = []
for i in range(particle_count):
particle = []
for j in range(6):
particle.append(random.uniform(-1, 1))
particles.append(particle)
return particles
def evaluate_fitness(particle, data):
fx, fy, cx, cy, k1, k2 = particle
error = 0
for i in range(len(data)):
X, Y, x, y = data[i]
r2 = (X*X) + (Y*Y)
x_distorted = x * (1 + k1*r2 + k2*r2*r2)
y_distorted = y * (1 + k1*r2 + k2*r2*r2)
x_reprojected = (fx*X + cx)/Y
y_reprojected = (fy*Y + cy)/Y
error += math.sqrt((x_reprojected - x_distorted)**2 + (y_reprojected - y_distorted)**2)
return error
def update_velocity(particle, best_particle, global_best_particle, w, c1, c2):
for i in range(len(particle)):
r1 = random.uniform(0, 1)
r2 = random.uniform(0, 1)
particle[i] = w*particle[i] + c1*r1*(best_particle[i] - particle[i]) + c2*r2*(global_best_particle[i] - particle[i])
return particle
def update_position(particle):
fx, fy, cx, cy, k1, k2 = particle
fx += random.uniform(-0.1, 0.1)
fy += random.uniform(-0.1, 0.1)
cx += random.uniform(-10, 10)
cy += random.uniform(-10, 10)
k1 += random.uniform(-0.1, 0.1)
k2 += random.uniform(-0.1, 0.1)
return [fx, fy, cx, cy, k1, k2]
def run_pso(data, particle_count, max_iterations):
particles = generate_initial_particles(particle_count)
best_particle = particles[0]
global_best_particle = particles[0]
for i in range(max_iterations):
for j in range(particle_count):
fitness = evaluate_fitness(particles[j], data)
best_fitness = evaluate_fitness(best_particle, data)
global_best_fitness = evaluate_fitness(global_best_particle, data)
if fitness < best_fitness:
best_particle = particles[j]
if fitness < global_best_fitness:
global_best_particle = particles[j]
for j in range(particle_count):
particles[j] = update_velocity(particles[j], best_particle, global_best_particle, 0.7, 1.4, 1.4)
particles[j] = update_position(particles[j])
return global_best_particle
if __name__ == '__main__':
data = load_data()
particle_count = 20
max_iterations = 100
best_particle = run_pso(data, particle_count, max_iterations)
fx, fy, cx, cy, k1, k2 = best_particle
print('fx:', fx)
print('fy:', fy)
print('cx:', cx)
print('cy:', cy)
print('k1:', k1)
print('k2:', k2)
```
在这个示例中,我们使用了一个名为 `load_data()` 的函数来从文件中加载标定数据。然后,我们使用 `generate_initial_particles(particle_count)` 函数来生成一组初始粒子。接下来,我们使用 `evaluate_fitness(particle, data)` 函数来计算每个粒子的适应度值。然后,我们使用 `update_velocity(particle, best_particle, global_best_particle, w, c1, c2)` 函数来更新粒子的速度,使用 `update_position(particle)` 函数来更新粒子的位置。最后,我们使用 `run_pso(data, particle_count, max_iterations)` 函数来运行粒子群优化算法,并找到最佳的相机参数。