# Python program to find mean across the image channels
# import necessary libraries
import torch
from PIL import Image
import torchvision.transforms as transforms

# Read the input image
img = Image.open('opera.jpg')

# Define transform to convert the image to PyTorch Tensor
transform = transforms.ToTensor()

# Convert image to PyTorch Tensor (Image Tensor)
imgTensor = transform(img)
print("Shape of Image Tensor:\n", imgTensor.shape)

# Compute mean of the Image Tensor across image channels RGB
R_mean, G_mean ,B_mean = torch.mean(imgTensor, dim = [1,2])

# print mean across image channel RGB
print("Mean across Read channel:", R_mean)
print("Mean across Green channel:", G_mean)
print("Mean across Blue channel:", B_mean)

Shape of Image Tensor:
   torch.Size([3, 447, 640])
Mean across Read channel: tensor(0.1487)
Mean across Green channel: tensor(0.1607)
Mean across Blue channel: tensor(0.2521)

# Python程序：查找图像通道的均值
# 导入必要的库
import torch
import cv2
import torchvision.transforms as transforms

# 使用cv2或PIL读取输入图像
img = cv2.imread('opera.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# 定义转换以将图像转换为PyTorch Tensor
transform = transforms.ToTensor()

# 将图像转换为PyTorch Tensor（Image Tensor）
imgTensor = transform(img)
print("Image Tensor的形状：\n", imgTensor.shape)

# 计算图像通道RGB的Image Tensor的均值
# 另一种计算均值的方式是
R_mean，G_mean，B_mean = imgTensor.mean(dim = [1,2])

# 输出RGB通道的均值
print("Red通道的平均值：", R_mean)
print("Green通道的平均值：", G_mean)
print("Blue通道的平均值：", B_mean)

Image Tensor的形状：
   torch.Size([3, 447, 640])
Red通道的平均值：tensor(0.1487)
Green通道的平均值：tensor(0.1607)
Blue通道的平均值：tensor(0.2521)