correlation imshow

你可以使用`matplotlib`库中的`imshow`函数来绘制相关性矩阵的热图。首先，你需要计算相关性矩阵，然后使用`imshow`函数将其可视化。

以下是一个简单的示例代码：

import numpy as np
import matplotlib.pyplot as plt

# 生成随机的相关性矩阵
correlation_matrix = np.random.rand(10, 10)

# 绘制热图
plt.imshow(correlation_matrix, cmap='hot', interpolation='nearest')
plt.colorbar()
plt.show()

在这个示例代码中，我们生成一个10x10的随机相关性矩阵，并使用热图进行可视化。你可以根据自己的数据替换相关性矩阵部分，并根据需要调整参数。

这只是一个简单的示例，进一步的定制和美化可以根据你的需求进行。希望对你有所帮助！如果还有其他问题，请随时提问。

相关问题

import cv2 import numpy as np import matplotlib.pyplot as plt image_path = './Lenna.jpg' image = cv2.imread(image_path) num_row, num_col, num_ch = image.shape # image channels are in BGR B = image[:, :, 0] G = image[:, :, 1] R = image[:, :, 2] # change the channel order from BGR to RGB and restore # CODE HERE image = cv2.merge([R, G, B]) fig = plt.figure(figsize=(11, 9)) fig.suptitle('Color image and RGB channel') ax = fig.add_subplot(2, 2, 1) ax.imshow(image) ax.axis('off') ax.axis('equal') ax.set_title('color image') # display the red channel in grayscale ax = fig.add_subplot(2, 2, 2) ax.imshow(R, cmap='gray') ax.axis('off') ax.axis('equal') ax.set_title('Channel R') # display the green channel in grayscale ax = fig.add_subplot(2, 2, 3) ax.imshow(G, cmap='gray') ax.axis('off') ax.axis('equal') ax.set_title('Channel G') # display the blue channel in grayscale ax = fig.add_subplot(2, 2, 4) ax.imshow(B, cmap='gray') ax.axis('off') ax.axis('equal') ax.set_title('Channel B') plt.pause(0) # calculate the mean value, variance and covirances # CODE HERE # Decomment and complete the following lines corr_RG = corr_GB = corr_BR = # Decomment the following lines print('The correlation between red and green is: ' + str(corr_RG)) print('The correlation between green and blue is: ' + str(corr_GB)) print('The correlation between blue and red is: ' + str(corr_BR)) # total contrast: # CODE HERE # proportions of each channel to the total contrast # Decomment and complete the following lines print('The propotion of red channel is: ' + str(CODE HERE)) print('The propotion of green channel is: ' + str(CODE HERE)) print('The propotion of blue channel is: ' + str(CODE HERE))

代码中有一些缺失的部分需要填写，我来帮你看一下。

import cv2 
import numpy as np 
import matplotlib.pyplot as plt  

image_path = './Lenna.jpg' 
image = cv2.imread(image_path)  
num_row, num_col, num_ch = image.shape  
# image channels are in BGR 
B = image[:, :, 0] 
G = image[:, :, 1] 
R = image[:, :, 2]  

# change the channel order from BGR to RGB and restore 
image = cv2.merge([R, G, B])  

fig = plt.figure(figsize=(11, 9)) 
fig.suptitle('Color image and RGB channel') 

ax = fig.add_subplot(2, 2, 1) 
ax.imshow(image) 
ax.axis('off') 
ax.axis('equal') 
ax.set_title('color image')  

# display the red channel in grayscale 
ax = fig.add_subplot(2, 2, 2) 
ax.imshow(R, cmap='gray') 
ax.axis('off') 
ax.axis('equal') 
ax.set_title('Channel R')  

# display the green channel in grayscale 
ax = fig.add_subplot(2, 2, 3) 
ax.imshow(G, cmap='gray') 
ax.axis('off') 
ax.axis('equal') 
ax.set_title('Channel G')  

# display the blue channel in grayscale 
ax = fig.add_subplot(2, 2, 4) 
ax.imshow(B, cmap='gray') 
ax.axis('off') 
ax.axis('equal') 
ax.set_title('Channel B')  

plt.pause(0)  

# calculate the mean value, variance and covirances 
mean_R = np.mean(R)
mean_G = np.mean(G)
mean_B = np.mean(B)

var_R = np.var(R)
var_G = np.var(G)
var_B = np.var(B)

cov_RG = np.cov(R.flatten(), G.flatten())[0][1]
cov_GB = np.cov(G.flatten(), B.flatten())[0][1]
cov_BR = np.cov(B.flatten(), R.flatten())[0][1]

# Decomment the following lines 
corr_RG = cov_RG / np.sqrt(var_R * var_G)
corr_GB = cov_GB / np.sqrt(var_G * var_B)
corr_BR = cov_BR / np.sqrt(var_B * var_R)

print('The correlation between red and green is: ' + str(corr_RG))
print('The correlation between green and blue is: ' + str(corr_GB))
print('The correlation between blue and red is: ' + str(corr_BR))

# total contrast:
contr = np.sqrt(var_R + var_G + var_B)

# proportions of each channel to the total contrast 
prop_R = np.sqrt(var_R) / contr
prop_G = np.sqrt(var_G) / contr
prop_B = np.sqrt(var_B) / contr

print('The propotion of red channel is: ' + str(prop_R))
print('The propotion of green channel is: ' + str(prop_G))
print('The propotion of blue channel is: ' + str(prop_B))

你需要填写的部分已经在注释中标出，其中：

- 需要计算各个通道的均值、方差和协方差矩阵，这部分可以使用 numpy 中的相关函数来实现；
- 需要计算各个通道之间的相关系数，通过协方差和各自的方差计算即可；
- 需要计算总对比度和各个通道对总对比度的贡献，这里的总对比度可以使用各个通道方差的和的平方根来计算。

% Read two images %image1 = imread('1.png'); %image2 = imread('2.png'); image1 = imread('40.bmp'); image2 = imread('乙醇.bmp'); % Down-sample the image to half its original resolution downsampled_image1 = imresize(image1, 0.1); downsampled_image2 = imresize(image2, 0.1); % Convert images to grayscale image1 = rgb2gray(downsampled_image1); image2 = rgb2gray(downsampled_image2); % Convert images to double precision for computations image1 = double(image1); image2 = double(image2); % Determine size of images [n, m] = size(image1); % Initialize matrices for displacement fields u = zeros(n, m); v = zeros(n, m); % Set window size for correlation (odd number to have a central pixel) window_size = 15; half_window_size = (window_size-1)/2; % You need to initialize these variables before the loop uTemp = zeros(n, m); vTemp = zeros(n, m); for i = 1+half_window_size : n-half_window_size fprintf('The value of i is: %d\n', i); parfor j = 1+half_window_size : m-half_window_size fprintf('The value of j is: %d\n', j); % Extract sub-window from image1 sub_window1 = image1(i-half_window_size : i+half_window_size, j-half_window_size : j+half_window_size); % Skip this sub-window if all its values are the same if numel(unique(sub_window1)) == 1 continue; end % Correlate this with image2 within a search area (here, the whole image) correlation = normxcorr2(sub_window1, image2); % Find the peak correlation [ypeak, xpeak] = find(correlation == max(correlation(:))); % If there are multiple, just take the first one ypeak = ypeak(1); xpeak = xpeak(1); % Compute displacements (be careful about off-by-one due to zero-based and one-based indexing) uTemp(i,j) = ypeak - i; vTemp(i,j) = xpeak - j; end end % Copy the temporary variables back to the original ones after the loop u = uTemp; v = vTemp; % Flatten the images into 1D arrays image1_1D = image1(:); image2_1D = image2(:); % Compute the correlation coefficient correlationCoefficient = corrcoef(image1_1D, image2_1D); % The correlation coefficient is the value at position (1,2) or (2,1) in the output matrix correlationCoefficient = correlationCoefficient(1,2); fprintf('The value of correlationCoefficient is: %d\n', correlationCoefficient); % Display original images and displacement field figure, subplot(1,3,1), imshow(image1, []), title('Image 1'); subplot(1,3,2), imshow(image2, []), title('Image 2'); subplot(1,3,3), quiver(u, v), title('Displacement Field');

这段代码是一个基于互相关的图像位移估计算法的实现。这个算法的基本思路是在一个图像中选取一个子窗口，在另一个图像中搜索与之最相似的子窗口，通过计算它们之间的位移来估计图像的位移。具体实现中，首先读取两张图像，并对它们进行降采样和灰度化处理。然后，通过计算互相关来找到最相似的子窗口，并计算它们之间的位移。最后，将位移场可视化并计算两张图像的相关系数。

代码中的parfor循环是一个并行循环，用于加速计算。它会自动地将循环中的任务分配到多个处理器上并行执行。在这个例子中，它会将每个像素的处理任务分配到多个处理器上并行执行，以提高计算速度。