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84 lines
3.0 KiB
84 lines
3.0 KiB
3 years ago
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import numpy as np
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import cv2 as cv
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import matplotlib.pyplot as plt
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def frequency_filter(image, filter):
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"""
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:param image:
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:param filter: 频域变换函数
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:return:
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"""
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fftImg = np.fft.fft2(image) # 对图像进行傅里叶变换
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fftImgShift = np.fft.fftshift(fftImg) # 傅里叶变换后坐标移动到图像中心
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handle_fftImgShift1 = fftImgShift*filter # 对傅里叶变换后的图像进行频域变换
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handle_fftImgShift2 = np.fft.ifftshift(handle_fftImgShift1)
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handle_fftImgShift3 = np.fft.ifft2(handle_fftImgShift2)
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handle_fftImgShift4 = np.real(handle_fftImgShift3) # 傅里叶反变换后取频域
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return np.uint8(handle_fftImgShift4)
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# 理想低通滤波器
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def ILPF(image, d0, n):
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H = np.empty_like(image, dtype=float)
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M, N = image.shape
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mid_x = int(M/2)
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mid_y = int(N/2)
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for y in range(0, M):
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for x in range(0, N):
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d = np.sqrt((x - mid_x) ** 2 + (y - mid_y) ** 2)
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if d <= d0:
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H[y, x] = 1**n
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else:
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H[y, x] = 0**n
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return H
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# 巴特沃斯低通滤波器
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def BLPF(image, d0, n):
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H = np.empty_like(image, float)
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M, N = image.shape
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mid_x = int(M/2)
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mid_y = int(N/2)
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for y in range(0, M):
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for x in range(0, N):
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d = np.sqrt((x - mid_x) ** 2 + (y - mid_y) ** 2)
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H[y, x] = 1/(1+(d/d0)**(n))
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return H
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# 高斯低通滤波器
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def GLPF(image, d0, n):
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H = np.empty_like(image, float)
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M, N = image.shape
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mid_x = M/2
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mid_y = N/2
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for x in range(0, M):
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for y in range(0, N):
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d = np.sqrt((x - mid_x)**2 + (y - mid_y) ** 2)
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H[x, y] = np.exp(-d**n/(2*d0**n))
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return H
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# 读取图像
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image = cv.imread('D:/Python/FrequencyDomainProcessing/img/moon.jpg')
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img_RGB = cv.cvtColor(image, cv.COLOR_BGR2RGB) # 转成RGB 方便后面显示
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grayImage = cv.cvtColor(img_RGB, cv.COLOR_BGR2GRAY)
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result2 = frequency_filter(grayImage, ILPF(grayImage, 60, n=1))
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result3 = frequency_filter(grayImage, BLPF(grayImage, 60, n=2))
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result4 = frequency_filter(grayImage, BLPF(grayImage, 90, n=2))
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result5 = frequency_filter(grayImage, GLPF(grayImage, 60, n=2))
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result6 = frequency_filter(grayImage, GLPF(grayImage, 90, n=2))
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# 显示图形
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.subplot(321), plt.imshow(grayImage, cmap=plt.cm.gray), plt.title('原始图像'), plt.axis('off') # 坐标轴关闭
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plt.subplot(322), plt.imshow(result2, cmap=plt.cm.gray), plt.title('理想低通滤波(D0=60)'), plt.axis('off')
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plt.subplot(323), plt.imshow(result3, cmap=plt.cm.gray), plt.title('Butterwoth低通(D0=60,n=2)'), plt.axis('off')
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plt.subplot(324), plt.imshow(result4, cmap=plt.cm.gray), plt.title('Butterwoth低通(D0=90,n=2)'), plt.axis('off')
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plt.subplot(325), plt.imshow(result5, cmap=plt.cm.gray), plt.title('Gauss低通(D0=60,n=2)'), plt.axis('off')
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plt.subplot(326), plt.imshow(result6, cmap=plt.cm.gray), plt.title('Gauss低通(D0=90,n=2)'), plt.axis('off')
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plt.show()
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