基于Python实现图像的傅里叶变换

"""
Author:XiaoMa
date:2021/11/7
"""
import cv2
import matplotlib.pyplot as plt
import numpy as np
 
#读取图像信息
from numpy.fft import ifftshift
 
img0 = cv2.imread("E:\From Zhihu\For the desk\cvthirteen2.jpg")
img1 = cv2.resize(img0, dsize = None, fx = 0.5, fy = 0.5)
img2 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)               #转化为灰度图
h, w = img1.shape[:2]
print(h, w)
cv2.namedWindow("W0")
cv2.imshow("W0", img2)
cv2.waitKey(delay = 0)
#将图像转化到频域内并绘制频谱图
##numpy实现
plt.rcParams['font.family'] = 'SimHei'       #将全局中文字体改为黑体
f = np.fft.fft2(img2)
fshift = np.fft.fftshift(f)   #将0频率分量移动到图像的中心
magnitude_spectrum0 = 20*np.log(np.abs(fshift))
#傅里叶逆变换
#Numpy实现
ifshift = np.fft.ifftshift(fshift)
# 将复数转为浮点数进行傅里叶频谱图显示
ifimg = np.log(np.abs(ifshift))
if_img = np.fft.ifft2(ifshift)
origin_img = np.abs(if_img)
imggroup = [img2, magnitude_spectrum0, ifimg, origin_img]
titles0 = ['原始图像', '经过移动后的频谱图', '逆变换得到的频谱图', '逆变换得到的原图']
for i in range(4):
    plt.subplot(2, 2, i + 1)
    plt.xticks([])                               #除去刻度线
    plt.yticks([])
    plt.title(titles0[i])
    plt.imshow(imggroup[i], cmap = 'gray')
plt.show()
##OpenCV实现
dft = cv2.dft(np.float32(img2), flags = cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)
magnitude_spectrum1 = 20*np.log(cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))
plt.subplot(121), plt.imshow(img2, cmap = 'gray')
plt.title('原图'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(magnitude_spectrum1, cmap = 'gray')
plt.title('频谱图'), plt.xticks([]), plt.yticks([])
plt.show()

"""
Author:XiaoMa
date:2021/11/7
"""
import cv2
import matplotlib.pyplot as plt
import numpy as np
 
#读取图像信息
img0 = cv2.imread("E:\From Zhihu\For the desk\cvthirteen2.jpg")
img1 = cv2.resize(img0, dsize = None, fx = 0.5, fy = 0.5)
img2 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)               #转化为灰度图
h, w = img1.shape[:2]
print(h, w)
cv2.namedWindow("W0")
cv2.imshow("W0", img1)
cv2.waitKey(delay = 0)

#将图像转化到频域内并绘制频谱图
plt.rcParams['font.family'] = 'SimHei'       #将全局中文字体改为黑体
f = np.fft.fft2(img2)
fshift = np.fft.fftshift(f)       #将0频率分量移动到中心
magnitude_spectrum = 20*np.log(np.abs(fshift))
plt.xticks([])                               #除去刻度线
plt.yticks([])
plt.title("频谱图")
plt.imshow(magnitude_spectrum, cmap = 'gray')
plt.show()

#OpenCV实现
dft = cv2.dft(np.float32(img2), flags = cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)
magnitude_spectrum1 = 20*np.log(cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))
plt.subplot(121), plt.imshow(img2, cmap = 'gray')
plt.title('原图'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(magnitude_spectrum1, cmap = 'gray')
plt.title('频谱图'), plt.xticks([]), plt.yticks([])
plt.show()

#傅里叶逆变换
#Numpy实现
ifshift = np.fft.ifftshift(fshift)
# 将复数转为浮点数进行傅里叶频谱图显示
ifimg = np.log(np.abs(ifshift))
if_img = np.fft.ifft2(ifshift)
origin_img = np.abs(if_img)
imggroup = [img2, magnitude_spectrum0, ifimg, origin_img]
titles0 = ['原始图像', '经过移动后的频谱图', '逆变换得到的频谱图', '逆变换得到的原图']
for i in range(4):
    plt.subplot(2, 2, i + 1)
    plt.xticks([])                               #除去刻度线
    plt.yticks([])
    plt.title(titles0[i])
    plt.imshow(imggroup[i], cmap = 'gray')
plt.show()