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

h.Title = 'Correlation Coefficient';

您好！根据您的代码，您可能想设置热图的标题为"Correlation Coefficient"。在Matplotlib中，可以通过设置`title`属性来实现这一目的。

以下是一个示例代码片段，演示了如何设置热图的标题为"Correlation Coefficient"：

import matplotlib.pyplot as plt

# 热图数据
xvalues = [1, 2, 3]
yvalues = [1, 2, 3]
rho = [[0.5, 0.3, 0.8], [0.2, 0.9, 0.4], [0.7, 0.6, 0.1]]

# 绘制热图
heatmap = plt.imshow(rho)

# 设置标题
plt.title('Correlation Coefficient')

# 显示热图
plt.show()

请注意，这只是一个示例代码片段，您需要根据您的实际需求进行调整。希望对您有所帮助！如果还有其他问题，请随时提问。

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。