如何创建自定义带通滤波器？

Question

在本研究论文中,在4.1节(预处理)中,给出了带通滤波器的等式:

哪里,

现在,我已经实现了以下内容:

https://dotnetfiddle.net/ZhucE2

但是,这段代码什么都没产生.

Answer 1

您需要创建内核的图像,然后将其与图像进行卷积.fft用于优化大图像的卷积.你可以使用filter2D函数让opencv为你做所有事情.

内核映像:

来源图片:

应用卷积:

Trhesholding:

请参阅以下代码:

import cv2
import math
import numpy as np

class Kernel(object):
    def H_Function(self, Dh, Dv, u, v, centerX, centerY, theta, n):
        return 1 / (1 + 0.414 * math.sqrt(math.pow(self.U_Star(u, centerX, centerY, theta) / Dh + self.V_Star(v, centerX, centerY, theta) / Dv, 2 * n)))

    def U_Star(self, u, centerX, centerY, theta):
        return math.cos(theta) * (u + self.Tx(centerX, theta)) + math.sin(theta) * (u + self.Ty(centerY, theta))

    def V_Star(self, u, centerX, centerY, theta):
        return (-math.sin(theta)) * (u + self.Tx(centerX, theta)) + math.cos(theta) * (u + self.Ty(centerY, theta))

    def Tx(self, center, theta):
        return center * math.cos(theta)

    def Ty(self, center, theta):
        return center * math.sin(theta)

K = Kernel()

size = 40, 40
kernel = np.zeros(size, dtype=np.float)
Dh=2
Dv=2
centerX = -size[0] / 2
centerY = -size[1] / 2
theta=0.9
n=4

for u in range(0, size[0]):
    for v in range(0, size[1]):
        kernel[u][v] = K.H_Function(Dh, Dv, u, v, centerX, centerY, theta, n) 
kernelNorm = np.copy(kernel)
cv2.normalize(kernel, kernel, 1.0, 0, cv2.NORM_L1)
cv2.normalize(kernelNorm, kernelNorm, 0, 255, cv2.NORM_MINMAX)
cv2.imwrite("kernel.jpg", kernelNorm)

imgSrc = cv2.imread('src.jpg',0)

convolved = cv2.filter2D(imgSrc,-1,kernel)
cv2.normalize(convolved, convolved, 0, 255, cv2.NORM_MINMAX)
cv2.imwrite("conv.jpg", convolved)
th, thresholded = cv2.threshold(convolved, 100, 255, cv2.THRESH_BINARY)
cv2.imwrite("thresh.jpg", thresholded)