pip安装方式清华镜像源

#理想低通滤波 ''' import cv2 import numpy as np import matplotlib.pyplot as plt def ideal_lowpass_filter(image, cutoff): # 获取图像的傅立叶变换 f = np.fft.fft2(image) fshift = np.fft.fftshift(f) # 构造理想低通滤波器掩模 rows, cols = image.shape center_row, center_col = int(rows / 2), int(cols / 2) mask = np.zeros((rows, cols), np.uint8) mask[center_row - cutoff:center_row + cutoff, center_col - cutoff:center_col + cutoff] = 1 # 应用滤波器 fshift_filtered = fshift * mask # 反傅立叶变换 f_ishift = np.fft.ifftshift(fshift_filtered) image_filtered = np.fft.ifft2(f_ishift) image_filtered = np.abs(image_filtered) return image_filtered # 读取图像 image = cv2.imread('img.png', cv2.IMREAD_GRAYSCALE) # 以灰度模式读取图像 # 应用理想低通滤波器 cutoff_frequency = 30 # 截止频率15 30 80 220 filtered_image = ideal_lowpass_filter(image, cutoff_frequency) # 显示原始图像和滤波后的图像 plt.subplot(121), plt.imshow(image, cmap='gray'), plt.title('Original Image') plt.axis('off') plt.subplot(122), plt.imshow(filtered_image, cmap='gray'), plt.title('Filtered Image') plt.axis('off') plt.show() ''' #理想滤波和巴特沃斯滤波对比 ''' import cv2 as cv import numpy as np from matplotlib import pyplot as plt plt.rcParams['font.sans-serif']=['SimHei'] plt.rcParams['axes.unicode_minus']=False def BLPF(fshift, D0, n): rows, cols = fshift.shape crow, ccol = rows // 2, cols // 2 H = np.zeros((rows, cols)) for u in range(rows): for v in range(cols): D = np.sqrt((u - crow) ** 2 + (v - ccol) ** 2) H[u, v] = 1 / (1 + (D / D0) ** (2 * n)) fshift1 = fshift * H return fshift1, H def ILBF(fshift,D0): rows,cols=fshift.shape crow,ccol=rows//2,cols//2 d0=D0**2 for u in range(rows): for v in range(cols): d =(u-crow)**2+(v-ccol)*2 if d>d0: fshift[u,v]=0+0j return fshift img=cv.imread('img.png',0) f=np.fft.fft2(img) fshift=np.fft.fftshift(f) magnitude_spectrum=20*np.log(1+np.abs(fshift)) #巴特沃斯低通滤波 fshift1,H=BLPF(fshift,20,2) f_ishift=np.fft.ifftshift(fshift1) img_back=np.fft.ifft2(f_ishift) img_back=np.abs(img_back) #理想低通滤波 fshift2=ILBF(fshift,20) f_ishift2=np.fft.ifftshift(fshift2) img_back2=np.fft.ifft2(f_ishift2) img_back2=np.abs(img_back2) plt.figure(figsize=(20,10)) plt.subplot(171),plt.imshow(img,cmap='gray') plt.title('原图像'),plt.axis("off") plt.subplot(172),plt.imshow(magnitude_spectrum,cmap ='gray') plt.title('原幅值谱') plt.axis("off") plt.subplot(173),plt.imshow(H,cmap='gray') plt.title('巴特沃斯传递函数'),plt.axis("off") plt.subplot(174),plt.imshow(20*np.log(1+np.abs(fshift1)),cmap ='gray') plt.title('滤波后的幅值谱') plt.axis("off") plt.subplot(175),plt.imshow(img_back,cmap ='gray') plt.title('巴特沃斯重构图像') plt.axis("off") plt.subplot(176),plt.imshow(img_back2,cmap='gray') plt.title('理想低通重构图像') plt.axis("off") plt.subplot(177),plt.imshow(20*np.log(1+np.abs(fshift2)),cmap ='gray') plt.title('理想低通幅值谱') plt.axis("off") plt.show() ''' #理想低通滤波空域响应 import cv2 import numpy as np import matplotlib.pyplot as plt def ideal_lowpass_filter(rows,cols,cutoff_frequency): ideal_filter=np.zeros((rows,cols)) #计算图像中心 center_x=rows //2 center_y=cols//2 #生成理想低通滤波器 for u in range(rows): for v in range(cols): distance=np.sqrt((u-center_x)**2 +(v-center_y)**2) if distance <cutoff_frequency: ideal_filter[u,v]=1 return ideal_filter #定义图像大小和截止频率 rows,cols=256,256#图像大小 cutoff_frequency=8#截止频率 #生成理想低通滤波器 ideal_filter=ideal_lowpass_filter(rows,cols,cutoff_frequency) #使用傅里叶反变换将频域响应转换为空间域响应 ideal_filter_spatial=np.fft.ifftshift(np.fft.ifft2(ideal_filter)) #取实部，因为逆变换结果是复数 ideal_filter_spatial=np.abs(ideal_filter_spatial) #可视化理想低通滤波器在空间域中的图像 plt.imshow(ideal_filter_spatial,cmap='gray') plt.title('Ideal Lowpass Filter in Spatial Domain') plt.axis('off') plt.show()