import numpy as np import cv2 def PSO_Gabor(func, x0, bounds, niters=100, nparticles=20, w=0.5, c1=1, c2=1): nparams = len(bounds) x = np.zeros((nparticles, nparams)) v = np.zeros_like(x) pbest = np.zeros_like(x) fitness = np.zeros(nparticles) gbest = np.zeros(nparams) gbest_fitness = np.inf for i in range(nparticles): x[i,:] = x0 + np.random.uniform(-1, 1, size=nparams) v[i,:] = np.random.uniform(-1, 1, size=nparams) pbest[i,:] = x[i,:] fitness[i] = func(x[i,:]) if fitness[i] < gbest_fitness: gbest_fitness = fitness[i] gbest = x[i,:] for _ in range(niters): for i in range(nparticles): v[i,:] = w*v[i,:] + c1*np.random.uniform(0,1,size=nparams)*(pbest[i,:] - x[i,:]) + c2*np.random.uniform(0,1,size=nparams)*(gbest - x[i,:]) x[i,:] = np.clip(x[i,:] + v[i,:], bounds[:,0], bounds[:,1]) fitness[i] = func(x[i,:]) if fitness[i] < pbest[i]: pbest[i,:] = x[i,:] if fitness[i] < gbest_fitness: gbest_fitness = fitness[i] gbest = x[i,:] return gbest def gabor_kernel(size, sigma, theta, lambd, gamma): """生成Gabor核""" kernel = cv2.getGaborKernel((size, size), sigma, theta, lambd, gamma, 0, ktype=cv2.CV_32F) return kernel def func(image, kernel): """使用Gabor核进行变换""" transformed = cv2.filter2D(image, cv2.CV_8UC3, kernel) return transformed image = cv2.imread('G:/python/python.wenjian/images/lw2.jpg', 0) best_params = PSO_Gabor(func, image, (image,2), niters=100, nparticles=20, w=0.5, c1=1, c2=1) print(gabor_kernel)这段代码有哪些问题
时间: 2023-05-22 13:02:43 浏览: 135
这段代码可能存在以下问题:
1. 当定义函数 gabor_kernel(size, sigma, theta, lambd, gamma) 时,没有使用函数参数,而是直接输出了 Gabor 核;
2. 函数 PSO_Gabor(func, x0, bounds, niters=100, nparticles=20, w=0.5, c1=1, c2=1) 的参数 bounds 值为图片和 2,可能不符合预期;
3. 函数 PSO_Gabor(func, x0, bounds, niters=100, nparticles=20, w=0.5, c1=1, c2=1) 的参数 x0 值为 image,但函数 func 的第一个参数应为变换后的图像,可能不符合需求;
4. 在调用 PSO_Gabor 函数时,传入参数 gabor_kernel 而不是 func,可能不符合预期。
以上就是可能存在的问题,有需要可以再讨论。
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