System(image_processing, "图像处理系统") { Container(low_pass_filter, "低通滤波器", "Python代码", "实现图像去噪") Container(image_storage, "图像存储", "数据库", "存储用户图像") } Rel(user, image_processing, "上传图像") Rel(image_processing, image_storage, "存储处理后的图像") 同时,在...
importcv2importnumpyasnpdeflow_pass_filter(image_path,kernel_size):img=cv2.imread(image_path)kernel=np.ones((kernel_size,kernel_size),np.float32)/(kernel_size*kernel_size)filtered_img=cv2.filter2D(img,-1,kernel)returnfiltered_img result=low_pass_filter('input.jpg',5)cv2.imwrite('filtered_...
shape)), cmap='gray')ax[0, 1].set_title('High Pass Filter of Diameter 50 px')ax[0, 1].set_xticks([])ax[0, 1].set_yticks([])# plot the third image in the bottom-left subplotim3 = ax[1, 0].imshow(np.abs(gaussianLP(50 ,img.shape)), cmap='gray')ax[1, 0].set_title...
它有的时候也被叫做低频去除过滤(low-cut filter)。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 defhigh_pass_filtering(image,radius,n):"""高通滤波函数:param image:输入图像:param radius:半径:param n:ButterWorth滤波器阶数:return:滤波结果""" # 对图像进行傅里叶变换,fft是一个三维数组,fft[:...
rows, cols = image.shape center_row, center_col = rows // 2, cols // 2 # 构造滤波器 low_pass_filter = np.zeros((rows, cols)) low_pass_filter[center_row - bandwidth // 2 : center_row + bandwidth // 2, center_col - bandwidth // 2 : center_col + bandwidth // 2] = 1 ...
plt.imshow(lowPassFilter(img,60),cmap="gray") 高通滤波器 高通滤波器同低通滤波器非常类似,只不过二者通过的波正好是相反的 H(u,v)={0,ifD(u,v)≤D01,ifD(u,v)≥D0H(u,v)={0,ifD(u,v)≤D01,ifD(u,v)≥D0 defhighPassFilter(image,d): ...
(ishift)4041res = cv2.magnitude(iimg[:,:,0], iimg[:,:,1])4243#显示原始图像和低通滤波处理图像4445plt.subplot(121), plt.imshow(img,'gray'), plt.title('Original Image')4647plt.axis('off')4849plt.subplot(122), plt.imshow(res,'gray'), plt.title('Low Pass Filter Image')5051plt....
下一个代码块使用scikit-image中的imread()函数读取uint8类型的numpy ndarray中的图像(8 位无符号整数)。因此,像素值将在 0 和 255 之间。然后使用Image.color模块的hsv2rgb()功能将彩色 RGB 图像转换为 HSV 图像(更改图像类型或模式,稍后讨论)。接下来,通过保持色调和值通道不变,将所有像素的饱和度(色度)...
# create low pass butteworth filter order = 2 fc = 30 # cut off frequency wc = 2*fc/fs # normalise [b,a] = signal.butter(order, wc, btype = 'lowpass') [w,h] = signal.freqz(b, a, worN = 1024) w = fs*w/(2*np.pi) # freq response plt.plot(w, 20*np.log10(h)) ...
# plot the first image in the top-left subplotim1 = ax[0, 0].imshow(np.abs(idealFilterLP(50, img.shape)), cmap='gray')ax[0, 0].set_title('Low Pass Filter of Diameter 50 px')ax[0, 0].set_xticks([])ax[0, 0].set_yticks(...