aa=(aa-min(aa))/(max(aa)-min(aa)); imshow(Img_View); max(aa);

clc; clear all; img_in = imread('ILSVRC2017_test_00000237.jpg'); m = size(img_in,1); n = size(img_in,2); img_lab = rgb2lab(img_in); img_L_mean = mean(mean(img_lab(:,:,1))); img_a_mean = mean(mean(img_lab(:,:,2))); img_b_mean = mean(mean(img_lab(:,:,3))); %高斯滤波 img_R = img_in(:,:,1); img_G = img_in(:,:,2); img_B = img_in(:,:,3); w = fspecial('gaussian',[7 7]); img_R_blur = imfilter(img_R,w); img_G_blur = imfilter(img_G,w); img_B_blur = imfilter(img_B,w); img_blur = cat(3,img_R_blur,img_G_blur,img_B_blur); figure('Name','滤波'); imshow(img_blur); img_lab_blur = rgb2lab(img_blur); %计算显著图 Sd = zeros(m,n); for i = 1:m for j = 1:n Sd(i,j) = sqrt((img_L_mean - img_lab_blur(i,j,1))^2 + (img_a_mean - img_lab_blur(i,j,2))^2 + (img_b_mean - img_lab_blur(i,j,3))^2); end end %归一化 Sd_normalized = figure_normalize(Sd); imwrite(Sd_normalized,'FT_saliency.jpg'); figure; imshow(Sd_normalized); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Out_image = figure_normalize(In_image) o_max_image = max(max(In_image)); o_min_image = min(min(In_image)); Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end运行该代码报错未定义函数或变量 'figure_normalize'。

Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end 注意：如果你使用的是 MATLAB 的版本较新，可能会有现成的函数可以实现这个功能，例如imadjust函数。你可以查看 MATLAB...

clc; clear all; img_in = imread('ILSVRC2017_test_00000237.jpg'); m = size(img_in,1); n = size(img_in,2); img_lab = rgb2lab(img_in); img_L_mean = mean(mean(img_lab(:,:,1))); img_a_mean = mean(mean(img_lab(:,:,2))); img_b_mean = mean(mean(img_lab(:,:,3))); %高斯滤波 img_R = img_in(:,:,1); img_G = img_in(:,:,2); img_B = img_in(:,:,3); w = fspecial('gaussian',[7 7]); img_R_blur = imfilter(img_R,w); img_G_blur = imfilter(img_G,w); img_B_blur = imfilter(img_B,w); img_blur = cat(3,img_R_blur,img_G_blur,img_B_blur); figure('Name','滤波'); imshow(img_blur); img_lab_blur = rgb2lab(img_blur); %计算显著图 Sd = zeros(m,n); for i = 1:m for j = 1:n Sd(i,j) = sqrt((img_L_mean - img_lab_blur(i,j,1))^2 + (img_a_mean - img_lab_blur(i,j,2))^2 + (img_b_mean - img_lab_blur(i,j,3))^2); end end %归一化 Sd_normalized = figure_normalize(Sd); imwrite(Sd_normalized,'FT_saliency.jpg'); figure; imshow(Sd_normalized); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Out_image = figure_normalize(In_image) o_max_image = max(max(In_image)); o_min_image = min(min(In_image)); Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end解释该代码中的matlab函数或变量 'figure_normalize'。

'figure_normalize' 是一个...其中，'o_max_image' 和 'o_min_image' 分别表示输入矩阵的最大值和最小值。函数的实现过程是将输入矩阵中的每个像素值减去最小值，然后除以最大值与最小值之差，得到归一化后的像素值。

请帮我优化一下这段代码if name=="main": input_dir = "./1.jpg" original_img, gray_img, RedThresh, closed, opened = Img_Outline(input_dir) box, draw_img = findContours_img(original_img,opened) #draw_img = findContours_img(original_img, opened) result_img = Perspective_transform(box,original_img) cv2.imshow("original", original_img) cv2.imshow("gray", gray_img) cv2.imshow("closed", closed) cv2.imshow("opened", opened) cv2.imshow("draw_img", draw_img) cv2.imshow("result_img", result_img) cv2.waitKey(0) cv2.destroyAllWindows()

cv2.imshow("result_img", result_img) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == "__main__": main() 这样可以使得代码更加清晰，每个函数只负责一个功能，易于维护和扩展。

img = cv.imread('pic/0.png') img_arr=np.array(img/255.0) #归一化 img_min=darkchannel(img_arr) #计算每个通道的最小值 img_dark=min_filter(img_min,2) #计算暗通道图像 img_guided=guided_filter(img_arr,img_min,r=75,eps=0.001) t,A=select_bright(img_min,img,w=0.95,t0=0.1,V=img_guided) dehaze=repair(img_arr,t,A) cv.imshow('Origin',img) cv.imshow('darkchannel',img_dark) cv.imshow('dehaze',dehaze) cv.waitKey() cv.destroyAllWindows()

接下来，调用min_filter函数对最小值图像img_min进行滤波处理，并将结果赋值给变量img_dark，得到暗通道图像。然后，调用guided_filter函数对归一化图像img_arr和最小值图像img_min进行导向滤波处理，...

close all; clear all; clc I = imread('D:\data\test\5.jpg'); I_r = double(I(:,:,1)); I_g = double(I(:,:,2)); I_b = double(I(:,:,3)); I_r_log = log(I_r+1); I_g_log = log(I_g+1); I_b_log = log(I_b+1); Rfft1 = fft2(I_r); Gfft1 = fft2(I_g); Bfft1 = fft2(I_b); % SSR算法 [m,n] = size(I_r); sigma = 200; f = fspecial('gaussian', [m, n], sigma); efft1 = fft2(double(f)); D_r = ifft2(Rfft1.efft1); D_g = ifft2(Gfft1.efft1); D_b = ifft2(Bfft1.*efft1); D_r_log = log(D_r + 1); D_g_log = log(D_g + 1); D_b_log = log(D_b + 1); R = I_r_log - D_r_log; G = I_g_log - D_g_log; B = I_b_log - D_b_log; R = exp(R); MIN = min(min(R)); MAX = max(max(R)); R = (R - MIN)/(MAX - MIN); R = adapthisteq(R); G = exp(G); MIN = min(min(G)); MAX = max(max(G)); G = (G - MIN)/(MAX - MIN); G = adapthisteq(G); B = exp(B); MIN = min(min(B)); MAX = max(max(B)); B = (B - MIN)/(MAX - MIN); B = adapthisteq(B); J = cat(3, R, G, B); figure; subplot(121);imshow(I); subplot(122);imshow(J); figure;imshow(J)

这段代码实现了一种图像增强算法，叫做 SSR (Single Scale Retinex)。主要思想是对原始图像进行对数变换，然后使用高斯滤波器对图像进行平滑处理，再将原始图像和平滑后的图像相减，得到反射分量。...

import cv2 import numpy as np import torch from skimage.segmentation import slic from skimage.util import img_as_float # 读取A图像和B图像 img_a = cv2.imread(r'D:\Users\Administrator\PycharmProjects\pythonProject\my tools\super_pixel\1.png') img_b = cv2.imread(r'D:\Users\Administrator\PycharmProjects\pythonProject\my tools\super_pixel\2.jpg') # 转换为浮点数 img_a = img_as_float(img_a) img_b = img_as_float(img_b) # 使用SLIC算法进行超像素分割 segments_a = slic(img_a, n_segments=1000, compactness=10) segments_b = slic(img_b, n_segments=1000, compactness=10) # 计算A图像的超像素范围 segment_ids = np.unique(segments_a) segment_ranges = [] for segment_id in segment_ids: y, x = np.where(segments_a == segment_id) min_x, max_x = np.min(x), np.max(x) min_y, max_y = np.min(y), np.max(y) segment_ranges.append((min_x, min_y, max_x, max_y)) # 创建A图像的超像素范围图 segment_map_a = np.zeros_like(segments_a, dtype=np.int32) for i, segment_range in enumerate(segment_ranges): min_x, min_y, max_x, max_y = segment_range segment_map_a[min_y:max_y+1, min_x:max_x+1] = i # 使用A图像的超像素范围索引对B图像进行分割 segment_map_b = np.zeros_like(segments_b, dtype=np.int32) for i, segment_range in enumerate(segment_ranges): min_x, min_y, max_x, max_y = segment_range segment_id = segments_a[min_y, min_x] y, x = np.where(segments_b == segment_id) segment_map_b[y, x] = i # 转换为PyTorch张量 segment_map_b = torch.Tensor(segment_map_b).long() # 显示B图像的超像素范围图 cv2.imshow('Segment Map', segment_map_b.numpy()) cv2.waitKey(0) cv2.destroyAllWindows()。上述代码出现错误： cv2.imshow('Segment Map', segment_map_b.numpy()) cv2.error: OpenCV(4.7.0) D:/a/opencv-python/opencv-python/opencv/modules/highgui/src/precomp.hpp:155: error: (-215:Assertion failed) src_depth != CV_16F && src_depth != CV_32S in function 'convertToShow'

这个错误是因为在使用cv2.imshow()函数时，需要传入的图像是8位无符号整数类型，而segment_map_b是一个PyTorch张量，需要将其转换为NumPy数组并将其数据类型转换为np.uint8类型。你可以使用以下代码将张量转换为...

修改此代码import cv2 import numpy as np import matplotlib.pyplot as plt img = cv2.imread(r'E:\\postgraduate\\three\\DIP3E_Original_Images_CH03\\3.10.tif', 0) def contrast_strech(src,r1,s1,r2,s2): height, width = src.shape dst = np.zeros((height, width), np.uint8) r_min, r_max = 0, 255 for i in range(img.shape[0]): for j in range(img.shape[1]): if img[i, j] > r_max: r_max = img[i, j] if img[i, j] < r_min: r_min = img[i, j] cv2.normalize(src, dst=dst, alpha=0, beta=255) dst=(255/(r_max-r_min)src[i,j]-(255r_min)/(r_max-r_min)) return dst image1 = contrast_strech(img,r_min,0,r_max,255) image2 = contrast_strech(img,(r_min+r_max)/2,0,(r_min+r_max)/2,255) plt.figure(figsize=(100,100)) plt.subplot(131) plt.imshow(img,cmap='gray') plt.axis('off') plt.subplot(132) plt.imshow(image1,cmap='gray') plt.axis('off') plt.subplot(133) plt.imshow(image2,cmap='gray') plt.axis('off') plt.show()

image2 = contrast_strech(img, (np.min(img) + np.max(img)) / 2, 0, (np.min(img) + np.max(img)) / 2, 255) plt.figure(figsize=(10, 10)) plt.subplot(131) plt.imshow(img, cmap='gray') plt.axis('off') plt....

修改此代码import cv2 import numpy as np import matplotlib.pyplot as plt img = cv2.imread(r'E:\\postgraduate\\three\\DIP3E_Original_Images_CH03\\3.10.tif', 0) def contrast_strech(src,r1,s1,r2,s2): height, width = src.shape dst = np.zeros((height, width), np.uint8) r_min, r_max = np.min(img),np.max(img) for i in range(0, height): for j in range(0, width): dst[i, j] = (255 - 0) / (r_max - r_min) * ((img(i, j) - r_min)) return dst image1 = contrast_strech(img, np.min(img), 0, np.max(img), 255) image2 = contrast_strech(img, (np.min(img) + np.max(img)) / 2, 0, (np.min(img) + np.max(img)) / 2, 255) plt.figure(figsize=(100,100)) plt.subplot(131) plt.imshow(img,cmap='gray') plt.axis('off') plt.subplot(132) plt.imshow(image1,cmap='gray') plt.axis('off') plt.subplot(133) plt.imshow(image2,cmap='gray') plt.axis('off') plt.show()

4. 在函数调用的时候，第二个对比度拉伸的函数调用应该为 image2 = contrast_strech(img, (np.min(img) + np.max(img)) / 2, 0, (np.min(img) + np.max(img)) / 2, 255) 而不是 image2 = contrast_strech(img, np...

Img_Bw=im2bw(Img_RG,0.1*graythresh(Img_RG)); Img_Bw=1-Img_Bw; figure;imshow(Img_Bw);title('r-b')

其中 Img_RG 是经过某些处理后得到的红绿通道分离的图像。graythresh 函数是用来自动计算阈值的，0.1 是一个系数，可以调整二值化的灵敏度。最后，imshow 函数用于显示图像，title 函数则添加了标题。

% 读取图像 img = imread('1.jpg'); % 转换为灰度图像 gray_img = rgb2gray(img); noise_img = imnoise(gray_img,'gaussian',0,0.01); % 设置正则化参数 lambda = 0.1; % 设置算法参数 maxIter = 1000; tol = 1e-6; % 初始化重建图像 recon_img = noise_img; for i = 1:maxIter % 计算梯度 grad = calcGrad(recon_img, noise_img); % 更新重建图像 recon_img = recon_img - lambda * grad; % 判断是否收敛 if norm(grad(:)) < tol break end end function grad = calcGrad(recon_img, noise_img) % 计算梯度 grad = 2 * (recon_img - noise_img); end % 显示重建结果 figure; subplot(1,3,1); imshow(gray_img); title('Original Image'); subplot(1,3,2); imshow(noise_img); title('Noisy Image'); subplot(1,3,3); imshow(recon_img); title('Reconstructed Image');错误: 文件:zxj.m 行:1 列:1 此上下文中不允许函数定义。

imshow(recon_img); 其中，my_reconstruction.m应该包含如下代码： matlab function recon_img = my_reconstruction(filename) % 读取图像 img = imread(filename); % 转换为灰度图像 gray_img = rgb2...

img=cv2.threshold(image,90,255,cv2.THRESH_BINARY) #调整裁剪效果阈值算法速度优于canny算法 # img = cv2.Canny(image,100,20) # cannyb = cv2.resize(b,None,fx=0.25,fy=0.25) # cv2.imshow('Canny', cannyb) # cv2.waitKey(0) binary_image=img[1] #二值图--具有三通道 # binary_image = img # binary_image=cv2.cvtColor(binary_image,cv2.COLOR_BGR2GRAY) # print(binary_image.shape) #改为单通道 x = binary_image.shape[1] print("宽度x=", x) y=binary_image.shape[0] print("高度y=",y) x_min = 0 y_min = 0 x_max = 0 y_max = 0 count = 0 for r in range(y): if 255 in binary_image[r][:]: if count == 0: y_min = r count += 1 y_max = r

int x_min = 0, y_min = 0, x_max = 0, y_max = 0, count = 0; for (int r = 0; r ; r++) { bool has_object = false; for (int c = 0; c < binary_image.cols; c++) { if (binary_image.at(r, c) == 255) { has...

解释这段代码img = imcomplement(img); se = strel('sphere',10); img = imdilate(img,se); img = conv2(double(img),double(l)); img = imresize(img,[28 28]); img(img < 0) = 0; img = img - min(img(:)); img = img./max(img(:)); img = uint8(255*img); imshow(img); imwrite(img, '.\\input.jpg'); img = reshape(img,784,1); set(handles.edit2,'string','...'); out_num = snn_scom(img);5 out_str=num2str(out_num); set(handles.edit2,'string',out_str);

img = img - min(img(:)); img = img./max(img(:)); % 将图像像素值转换为无符号8位整数（0-255） img = uint8(255*img); % 显示图像 imshow(img); % 将图像保存到指定路径 imwrite(img, '.\\input.jpg'); % 将...

import cv2 import numpy as np img=cv2.imread('../01.jpg') # list_name=['H_min','H_max','S_min','S_max','V_min','V_max'] img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) def onchange(x): kernel=np.ones((5,5)) h_min=cv2.getTrackbarPos('H_min','img') h_max=cv2.getTrackbarPos('H_max','img') s_min=cv2.getTrackbarPos('S_min','img') s_max=cv2.getTrackbarPos('S_max','img') v_min=cv2.getTrackbarPos('V_min', 'img') v_max=cv2.getTrackbarPos('V_max', 'img') lower=np.array([h_min,s_min,v_min],dtype=np.uint8) upper=np.array([h_max,s_max,v_max],dtype=np.uint8) mask=cv2.inRange(img_hsv,lower,upper) # mask=cv2.morphologtEx(mask,cv2.MORPH_OPEN,kernel) cv2.imshow('img',mask) cv2.namedWindow('img') cv2.createTrackbar('H_min','img', 1, 255,onchange) cv2.createTrackbar('H_max','img',1,255,onchange) cv2.createTrackbar('S_min','img',1,255,onchange) cv2.createTrackbar('S_max','img',1,255,onchange) cv2.createTrackbar('V_min','img',1,255,onchange) cv2.createTrackbar('V_max','img',1,255,onchange) cv2.destroyAllWindows()这段代码有错误吗

h_min=cv2.getTrackbarPos('H_min','img') h_max=cv2.getTrackbarPos('H_max','img') s_min=cv2.getTrackbarPos('S_min','img') s_max=cv2.getTrackbarPos('S_max','img') v_min=cv2.getTrackbarPos('V_min', 'img')...

import cv2 import matplotlib.pyplot as plt import numpy as np from skimage.measure import label, regionprops file_url = './data/origin/DJI_0081.jpg' output_url = './DJI_0081_ROI.jpg' def show_img(img, title): cv2.namedWindow(title, cv2.WINDOW_NORMAL) cv2.imshow(title, img) def output_img(img, url): cv2.imwrite(url, img, [int(cv2.IMWRITE_PNG_COMPRESSION), 9]) # 使用2g-r-b分离 src = cv2.imread(file_url) show_img(src, 'src') # 转换为浮点数进行计算 fsrc = np.array(src, dtype=np.float32) / 255.0 (b, g, r) = cv2.split(fsrc) gray = 2 * g - 0.9 * b - 1.1 * r # 求取最大值和最小值 (minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray) # 转换为u8类型，进行otsu二值化 gray_u8 = np.array((gray - minVal) / (maxVal - minVal) * 255, dtype=np.uint8) (thresh, bin_img) = cv2.threshold(gray_u8, -1.0, 255, cv2.THRESH_OTSU) show_img(bin_img, 'bin_img') def find_max_connected_component(binary_img): # 输出二值图像中所有的连通域 img_label, num = label(binary_img, connectivity=1, background=0, return_num=True) # connectivity=1--4 connectivity=2--8 # print('+++', num, img_label) # 输出连通域的属性，包括面积等 props = regionprops(img_label) resMatrix = np.zeros(img_label.shape).astype(np.uint8) # 只保留最大的连通域 max_area = 0 max_index = 0 for i in range(0, len(props)): if props[i].area > max_area: max_area = props[i].area max_index = i tmp = (img_label == max_index + 1).astype(np.uint8) resMatrix += tmp resMatrix *= 255 return resMatrix bin_img = find_max_connected_component(bin_img) show_img(bin_img, 'bin_img') # 得到彩色的图像 (b8, g8, r8) = cv2.split(src) color_img = cv2.merge([b8 & bin_img, g8 & bin_img, r8 & bin_img]) output_img(color_img, output_url) show_img(color_img, 'color_img') cv2.waitKey() cv2.destroyAllWindows()

这段代码是用来对一张图片进行处理，找到图片中最大的连通域，并将其保留下来。具体的处理过程如下： 1.读入图片并显示； 2.将图片转换成浮点数类型，方便做计算； 3.使用2g-r-b分离，得到灰度图像；...

aa=(aa-min(aa))/(max(aa)-min(aa)); imshow(Img_View); max(aa);

ndex=find(abs(aa-6.5423)>0.01); aa=aa(Index); Img_View=Img; [h,w,~]=size(Img_View); for i=1:h for j=1:w if Img_BW(i,j)~=0 Img_View(i,j,1)=Img_View(i,j,1)+255; Img_View(i,j,2)=Img_View(i,j,2); Img_View(i,j,3)=Img_View(i,j,3); end end end % aa=(aa-min(aa))/(max(aa)-min(aa)); imshow(Img_View); max(aa);

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