import numpy as npimport cv2# 读取图像img = cv2.imread('lena.png', 0)# 添加高斯噪声mean = 0var = 0.1sigma = var ** 0.5noise = np.random.normal(mean, sigma, img.shape)noisy_img = img + noise# 定义维纳滤波器函数def wiener_filter(img, psf, K=0.01): # 计算傅里叶变换 img_fft = np.fft.fft2(img) psf_fft = np.fft.fft2(psf) # 计算功率谱 img_power = np.abs(img_fft) ** 2 psf_power = np.abs(psf_fft) ** 2 # 计算信噪比 snr = img_power / (psf_power + K) # 计算滤波器 result_fft = img_fft * snr / psf_fft result = np.fft.ifft2(result_fft) # 返回滤波结果 return np.abs(result)# 定义维纳滤波器的卷积核kernel_size = 3kernel = np.ones((kernel_size, kernel_size)) / kernel_size ** 2# 计算图像的自相关函数acf = cv2.calcHist([img], [0], None, [256], [0, 256])# 计算维纳滤波器的卷积核gamma = 0.1alpha = 0.5beta = 1 - alpha - gammapsf = np.zeros((kernel_size, kernel_size))for i in range(kernel_size): for j in range(kernel_size): i_shift = i - kernel_size // 2 j_shift = j - kernel_size // 2 psf[i, j] = np.exp(-np.pi * ((i_shift ** 2 + j_shift ** 2) / (2 * alpha ** 2))) * np.cos(2 * np.pi * (i_shift + j_shift) / (2 * beta))psf = psf / np.sum(psf)# 对带噪声图像进行维纳滤波filtered_img = wiener_filter(noisy_img, psf)# 显示结果cv2.imshow('Original Image', img)cv2.imshow('Noisy Image', noisy_img)cv2.imshow('Filtered Image', filtered_img)cv2.waitKey(0)cv2.destroyAllWindows()这段代码报错为Traceback (most recent call last): File "<input>", line 1, in <module> File "D:\Pycharm\PyCharm 2020.3.5\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "D:\Pycharm\PyCharm 2020.3.5\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "E:/Python_project/class_/weinalvboqi.py", line 54, in <module> filtered_img = wiener_filter(noisy_img, psf) File "E:/Python_project/class_/weinalvboqi.py", line 25, in wiener_filter snr = img_power / (psf_power + K) ValueError: operands could not be broadcast together with shapes (1024,2800) (3,3)什么意思，如何修改

熟悉imread()函数，利用imread()来读取lena图像(在网上下载lena的灰度图像和彩色图像) (2)熟悉imshow()函数的几种用法，①把lena的灰度图像显示为2值图像、8级灰度图像和指定灰度区间[150,220]的灰度图像和用蓝色和绿色来表示二值图像(黑色用蓝色代表，白色用绿色代表)。②显示彩色的lena图像。 (3)利用imshow()函数的使用方式来显示原图像和变换后的图像，利用imwrite()来保存变换后的图像为bmp格式和png格式。

img_gray = cv.imread("lena.jpg", cv.IMREAD_GRAYSCALE) # 或使用参数0 2. **读取彩色图** python img_color = cv.imread("lena.jpg", cv.IMREAD_COLOR) # 或使用参数1 > 注意：OpenCV读取的彩色...

使用图像掩膜执行位运算，取出掩膜内的图像。实验步骤如下：步骤一，用cv2读取Lena.jpg的图像。步骤二，用cv2读取Lenamask.jpg的图像。步骤三，步骤一和步骤二中的图像按位与。步骤四，用cv2显示步骤一、步骤二、步骤三的图像。

2. 用cv2读取Lena.jpg的图像和Lenamask.jpg的图像。 python img = cv2.imread('Lena.jpg') mask = cv2.imread('Lenamask.jpg', cv2.IMREAD_GRAYSCALE) 3. 对掩膜进行二值化操作，将掩膜中非零值的像素设置...

读出一幅灰度图像（"lena_gray.gif"），给这幅图像分别加入椒盐噪声和高斯噪声后并与前一张图显示在同一图像窗口中

img = cv2.imread('lena_gray.gif', cv2.IMREAD_GRAYSCALE) # 添加椒盐噪声 pepper_salt_noise = np.copy(img) pepper_salt_noise[img ] = 0 pepper_salt_noise[img > 225] = 255 pepper_salt_noise = np.uint8...

解释代码import numpy as np import matplotlib.pyplot as plt # plt 用于显示图片 import matplotlib.image as mpimg # mpimg 用于读取图片 fig = plt.figure() #matplotlib只支持PNG图像 lena = mpimg.imread('cat.jpg') lena_r=np.zeros(lena.shape) #0通道 lena_r[:,:,0]=lena[:,:,0] ax1=fig.add_subplot(331) ax1.imshow(lena_r)# 显示R通道 lena_g=np.zeros(lena.shape)#1通道 lena_g[:,:,1]=lena[:,:,1] ax4=fig.add_subplot(334) ax4.imshow(lena_g)# 显示G通道 lena_b=np.zeros(lena.shape)#2通道 lena_b[:,:,2]=lena[:,:,2] ax7=fig.add_subplot(337) ax7.imshow(lena_b)# 显示B通道 img_R = lena_r[:,:,0] R_mean=np.mean(img_R) R_std=np.std(img_R) ax2=fig.add_subplot(332) flatten_r=img_R.flatten() weights = np.ones_like(flatten_r)/float(len(flatten_r)) prob_r,bins_r,_=ax2.hist(flatten_r,bins=10,facecolor='r',weights=weights) img_G = lena_g[:,:,1] G_mean=np.mean(img_G) G_std=np.std(img_G) ax5=fig.add_subplot(335) flatten_g=img_G.flatten() prob_g,bins_g,_=ax5.hist(flatten_g,bins=10,facecolor='g',weights=weights) img_B = lena_b[:,:,2] B_mean=np.mean(img_B) B_std=np.std(img_B) ax8=fig.add_subplot(338) flatten_b=img_B.flatten() prob_b,bins_b,_=ax8.hist(flatten_b,bins=10,facecolor='b',weights=weights) ax3=fig.add_subplot(233) rgb_mean=[R_mean,G_mean,B_mean] x_mlabel=['R_mean','G_mean','B_mean'] bar_width=0.5 bars_mean=ax3.bar(x_mlabel,rgb_mean,width=bar_width) colors=['r','g','b'] for bar,color in zip(bars_mean,colors): bar.set_color(color) ax3.set_title('Mean') ax9 = fig.add_subplot(236) rgb_std =[R_std,G_std,B_std] x_mlabel = ['R_std','G_std','B_std'] bar_width = 0.5 bars_std = ax9.bar(x_mlabel,rgb_std,width = bar_width) colors = ['r','g','b'] for bar,color in zip(bars_std,colors): bar.set_color(color) ax9.set_title('Std') # fig.set_tight_layout(True) plt.show()

这段代码主要是读取一张名为"cat.jpg"的图片，并对其RGB三个通道进行分析和统计。首先，将原图的RGB三个通道分别提取出来，然后在左上角、左中和左下角用subplot展示三个通道的图像。接着，分别计算每个通道的...

import cv2 import numpy as np import matplotlib.pyplot as plt def build_filters(): filters = [] ksize = 9 for theta in np.arange(0, np.pi, np.pi / 8): for sigma in range(1, 6): for frequency in (0.1, 0.2): kernel = cv2.getGaborKernel((ksize, ksize), sigma, theta, frequency, 0.5, 0, ktype=cv2.CV_32F) kernel /= 1.5 * kernel.sum() filters.append(kernel) return filters def process(img, filters): features = np.zeros((img.shape[0], img.shape[1], len(filters)), dtype=np.float32) for i, kernel in enumerate(filters): filtered = cv2.filter2D(img, cv2.CV_8UC3, kernel) features[:, :, i] = filtered return features # 读取图像并转为灰度图 img = cv2.imread('lena.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 构建gabor滤波器组 filters = build_filters() # 对图像进行gabor特征提取 features = process(gray, filters) # 显示gabor滤波后的特征图像 fig, axes = plt.subplots(nrows=5, ncols=8, figsize=(16, 10)) for i, ax in enumerate(axes.flat): ax.imshow(features[:, :, i], cmap='gray') ax.set_xticks([]) ax.set_yticks([]) plt.show()用矩阵将这段代码中特征提取后的图像表示出来

在这段代码中，使用了OpenCV库和NumPy库构建了Gabor滤波器组，然后对图像进行Gabor特征提取，并使用Matplotlib库将每个滤波器返回的特征图像显示出来。features是一个三维数组，其中第一维和第二维表示图像的高度和...

import numpy as np import matplotlib.pyplot as plt import pywt from skimage import io, color # 读取灰度图像并转换为RGB图像 img_gray = io.imread('lena.png', as_gray=True) img = color.gray2rgb(img_gray) # 对图像的三个通道进行DWT变换 coeffs_r = pywt.dwt2(img[:, :, 0], 'haar') coeffs_g = pywt.dwt2(img[:, :, 1], 'haar') coeffs_b = pywt.dwt2(img[:, :, 2], 'haar') # 设置压缩比率 compress_ratio = 0.5 # 计算阈值 threshold_r = np.sort(np.abs(coeffs_r[1].ravel()))[::-1][int(compress_ratio * len(coeffs_r[1].ravel()))] threshold_g = np.sort(np.abs(coeffs_g[1].ravel()))[::-1][int(compress_ratio * len(coeffs_g[1].ravel()))] threshold_b = np.sort(np.abs(coeffs_b[1].ravel()))[::-1][int(compress_ratio * len(coeffs_b[1].ravel()))] # 对小于阈值的系数进行置零 coeffs_r = list(coeffs_r) coeffs_r[0] = np.round(coeffs_r[0]) coeffs_r[1] = np.where(np.abs(coeffs_r[1]) < threshold_r, 0, coeffs_r[1]) coeffs_r[2] = np.where(np.abs(coeffs_r[2]) < threshold_r, 0, coeffs_r[2]) coeffs_g = list(coeffs_g) coeffs_g[0] = np.round(coeffs_g[0]) coeffs_g[1] = np.where(np.abs(coeffs_g[1]) < threshold_g, 0, coeffs_g[1]) coeffs_g[2] = np.where(np.abs(coeffs_g[2]) < threshold_g, 0, coeffs_g[2]) coeffs_b = list(coeffs_b) coeffs_b[0] = np.round(coeffs_b[0]) coeffs_b[1] = np.where(np.abs(coeffs_b[1]) < threshold_b, 0, coeffs_b[1]) coeffs_b[2] = np.where(np.abs(coeffs_b[2]) < threshold_b, 0, coeffs_b[2]) # 合并三个通道的系数 coeffs = [np.stack([coeffs_r[i], coeffs_g[i], coeffs_b[i]], axis=-1) for i in range(len(coeffs_r))] # 对图像进行IDWT反变换 img_dwt = pywt.idwt2(coeffs, 'haar') # 显示原始图像和压缩后的图像 fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) ax = axes.ravel() ax[0].imshow(img) ax[0].set_title("Original image") ax[1].imshow(img_dwt) ax[1].set_title("Compressed image (DWT)") ax[1].set_xlabel("Compression ratio: {:.2f}".format(compress_ratio)) plt.tight_layout() plt.show()

这段代码缺少了两行，分别是导入numpy和matplotlib.pyplot两个模块的语句。请在代码的开头添加以下两行语句： ...import numpy as np import matplotlib.pyplot as plt 这样就可以解决代码中的报错。

pycharm将import scipy.fftpack as fp from skimage.io import imread from skimage.color import rgb2gray, gray2rgb import matplotlib.pyplot as plt from skimage.draw import rectangle_perimeter import numpy as np import cv2 import numpy as np import matplotlib.pylab as plt from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import from mpl_toolkits.axes_grid1 import make_axes_locatable from skimage.metrics import peak_signal_noise_ratio, structural_similarity from sklearn.metrics import mean_squared_error, mean_absolute_error from matplotlib.ticker import LinearLocator, FormatStrFormatter from scipy.fftpack import dct, idct import cv2 as cv def dct2(a): return dct(dct(a, axis=0, norm='ortho'), axis=1, norm='ortho') def idct2(a): return idct(idct(a, axis=0, norm='ortho'), axis=1, norm='ortho') im = rgb2gray(imread('2.jpg')) imF = dct2(im) im1 = idct2(imF) print(np.allclose(im, im1))优化一下，能控制压缩率

im = cv2.imread('lena.png', cv2.IMREAD_GRAYSCALE) # 设置量化参数Q Q = 50 # 进行图像压缩 im1 = compress_image(im, Q) # 计算压缩比率 cr = im.size / (im1.size * np.ceil(np.log2(Q))) # 计算PSNR和SSIM psnr...

ImageId = '0002cc93b.jpg_3' img = cv2.imread('C:\Users\86178\Desktop\ImageData\A08数据集\A08数据集\train_images' + ImageId) print(img.shape) Cell In[21], line 2 img = cv2.imread('C:\Users\86178\Desktop\ImageData\A08数据集\A08数据集\train_images' + ImageId) ^ SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape 直接给我更改后的源码

return cv2.imdecode(image_np, cv2.IMREAD_COLOR) # 测试中文路径 chinese_path = "D:/测试目录/测试图片.jpg" img = imread_chinese_path(chinese_path) if img is not None: print("成功读取图片") else: ...

用s=L-1-r img = cv2.imread(r'.\images\lena_gray_256.tif')的代码进行图像反转，要求显示原图和反转后的图像

img = cv2.imread(r'.\images\lena_gray_256.tif') # 图像反转（位运算） inverted_img = cv2.bitwise_not(img) # 显示原图和反转后的图像 cv2.imshow("Original Image", img) cv2.imshow("Inverted Image", ...

import cv2 as cv img = cv.imread("C:/Users/dell/Desktop/lena512color.png") surf = cv.xfeatures2d.SURF_create(7000) kp, des = surf.detectAndCompute(img,None) img2 = cv.drawKeypoints(img,kp,None,(255,0,0),4) cv.imshow('surf', img2) cv.waitKey(0) cv.destroyAllWindows() 帮我修改一下这个代码然后导出图片

import numpy as np # 读取图像 img = cv2.imread('image.jpg') # 创建SURF对象（注意版本限制） surf = cv2.xfeatures2d.SURF_create() # 检测关键点和描述符 keypoints, descriptors = surf.detectAndCompute...

import numpy as np import cv2 def motion_blur(image, degree=7, angle=45): image = np.array(image) # 这里生成任意角度的运动模糊kernel的矩阵， degree越大，模糊程度越高 M = cv2.getRotationMatrix2D((degree / 2, degree / 2), angle, 1) motion_blur_kernel = np.diag(np.ones(degree)) motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (degree, degree)) motion_blur_kernel = motion_blur_kernel / degree blurred = cv2.filter2D(image, -1, motion_blur_kernel) # convert to uint8 cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX) blurred = np.array(blurred, dtype=np.uint8) return blurred def add_gaussian_noise(image, mean=0, std=10): noise = np.random.normal(mean, std, image.shape).astype(np.uint8) noisy_image = cv2.add(image, noise) return noisy_image img = cv2.imread('lena_gray.tif') # 修改运动模糊函数的调用参数 img_ = motion_blur(img, degree=7, angle=-45) noisy_img = add_gaussian_noise(img_) cv2.imshow('Original image', img) cv2.imshow('blur image', img_) cv2.imshow('blur and noisy image', noisy_img) cv2.waitKey() 使用直接逆滤波方法复原blur and noisy image，写出修改后的代码

img = cv2.imread('lena_gray.tif', cv2.IMREAD_GRAYSCALE) # 添加运动模糊和高斯噪声 img_, kernel = motion_blur(img, degree=7, angle=-45) noisy_img = add_gaussian_noise(img_) # 图像复原 restored_...

import cv2 import numpy as np import os img1 = cv2.imread("C:/Users/X/Desktop/lena.jpg") cv2.imshow("111",img1) img2 = np.zeros(img1.shape, dtype=np.uint8) img2[50:150,50:150] = 255 cv2.imshow("222",img2) result = cv2.bitwise_or(img1,img2) cv2.imshow("result",result) cv2.waitKey() cv2.destroyAllWindows()，分析这一段代码

- cv2.imread(filename, 0)：以灰度模式加载图像，返回二维矩阵$M_{h×w}$，参数0表示强制灰度化[^2] - 彩色模式读取应使用cv2.IMREAD_COLOR，得到三维矩阵$M_{h×w×3}$ 2. **掩码创建（inRange）** ...

使用加法去除高斯噪声读取一幅图像，添加随机高斯噪声，将多幅噪声图像叠加后取平均值，观察图像的去噪效果。image-3.png # 对image图像添加均值为0，方差为20-40之间的随机高斯噪声 # 根据iterations的值进行噪声图像的叠加 # var为方差，noise为随机高斯噪声，noisy_image为添加噪声的图像， # sum_noisy_image为多个噪声图像的累加和 def additive_noise(image,iterations): ########## Begin ########## sum_noisy_image = np.zeros_like(image, dtype=np.float32) for _ in range(iterations): var = np.random.uniform(20, 40) noise = np.random.normal(0, var**0.5, image.shape).astype(np.float32) noisy_image = image + noise sum_noisy_image += noisy_image return sum_noisy_image/iterations ########## End ########## return sum_noisy_image # 使用np.array读取图像/data/bigfiles/lena_Gray.jpg image = cv2.imread('/data/bigfiles/lena_Gray.jpg', cv2.IMREAD_GRAYSCALE) # 叠加噪声图像的次数 iterations1 = 5 iterations2 = 100 noisy_image1 = additive_noise(image, 1) # 1幅噪声图像 noisy_image5 = additive_noise(image, iterations1) # 5幅噪声图像叠加 noisy_image100 = additive_noise(image, iterations2) # 100幅噪声图像叠加 # 应用加法去噪 clean_image5 = noisy_image5 # 5幅噪声图像叠加后取平均值 clean_image100 = noisy_image100# 100幅噪声图像叠加后取平均值 # 按上图示例显示4幅图像 ########## Begin ########## plt.figure(figsize=(8, 8)) plt.subplot(2, 2, 1) plt.imshow(image, cmap='gray') plt.title('Original Image') plt.axis('off') # 不显示坐标轴 plt.subplot(2, 2, 2) plt.imshow(noisy_image1, cmap='gray') plt.title('Noisy Image') plt.axis('off') # 不显示坐标轴 plt.subplot(2, 2, 3) plt.imshow(clean_image5, cmap='gray') plt.title('Cleaned Image5') plt.axis('off') # 不显示坐标轴 plt.subplot(2, 2, 4) plt.imshow(clean_image100, cmap='gray') plt.title('Cleaned Image100') plt.axis('off') # 不显示坐标轴 plt.show() ########## End ##########

original_float = original_img.astype(np.float32) / 255.0 num_images = 100 noise_std = 0.1 # 噪声标准差，假设为0.1，范围是[0,1]因为原图被归一化了 # 生成多张带噪声的图像 noisy_images = [] for _ in ...

请修改下列代码，使其能够在Matlab软件上成功运行，保持代码间逻辑不变和最终的运行效果不变：import numpy as np import cv2 from matplotlib import pyplot as plt from matplotlib import font_manager font = font_manager.FontProperties(fname=r"c:\windows\fonts\msyh.ttc", size=10) img = cv2.imread('lena.jpg',0) img_float32 = np.float32(img) # 格式转换 # 傅里叶变换 dft = cv2.dft(img_float32, flags = cv2.DFT_COMPLEX_OUTPUT) # 频谱中心化 dft_shift = np.fft.fftshift(dft) # 得到灰度图能表示的形式 magnitude_spectrum1 = 20np.log(cv2.magnitude(dft[:,:,0],dft_shift[:,:,1])) magnitude_spectrum2 = 20np.log(cv2.magnitude(dft_shift[:,:,0],dft_shift[:,:,1])) plt.subplot(131),plt.imshow(img, cmap = 'gray'),plt.title('原图',fontproperties=font) plt.xticks([]), plt.yticks([]) plt.subplot(132),plt.imshow(magnitude_spectrum1, cmap = 'gray'),plt.title('傅里叶变换（未中心化）',fontproperties=font) plt.xticks([]), plt.yticks([]) plt.subplot(133),plt.imshow(magnitude_spectrum2, cmap = 'gray'),plt.title('傅里叶变换（中心化）',fontproperties=font) plt.xticks([]), plt.yticks([]) plt.show()

Python中的cv2.imread('./img/hand1.png',0)对应Matlab的灰度读取： matlab img = imread('./img/hand1.png'); if size(img, 3) == 3 % 若为RGB图像则转为灰度 img = rgb2gray(img); end img = im2double(img)...

imFFT = np.fft.fft2(im) imFFTshift = np.fft.fftshift(imFFT) imFFTabs = np.abs(imFFTshift) imFFTabs = 30*np.log(imFFTabs)接下来怎么做

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imFFT = np.fft.fft2(im) imFFTshift = np.fft.fftshift(imFFT) imFFTabs = np.abs(imFFTshift) imFFTabs = 30*np.log(imFFTabs)接下来怎么做

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读出一幅灰度图像（"lena_gray.gif"），给这幅图像分别加入椒盐噪声和高斯噪声后并与前一张图显示在同一图像窗口中

用s=L-1-r img = cv2.imread(r'.\images\lena_gray_256.tif')的代码进行图像反转，要求显示原图和反转后的图像

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