你应该用一个循环从0到width：

from PIL import Image

image = Image.open("1.png")
width, height = image.size
y = 137

brightness_values = []

# Loop through the horizontal line
for x in range(width):
    pixelRGB = image.getpixel((x, y))
    R, G, B = pixelRGB
    brightness = (R + G + B) / 3  
    brightness_values.append(brightness)

print(brightness_values)

average_brightness = sum(brightness_values) / len(brightness_values)
print(f"Average brightness: {average_brightness}")

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在Python中使用for循环处理图像速度慢且容易出错。请尝试使用Pillow的内置函数或向量化的Numpy函数。
首先，使用枕头：

from PIL import Image, ImageStat

# Create 256x256 image whose rows will have different means depending on the y-value
im = Image.radial_gradient('L').convert('RGB')

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的数据

# Crop out row 64
row64 = im.crop((0,64,im.width,65))

# Get statistics for that row
stats = ImageStat.Stat(row64)

# Print means
print(stats.mean)

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输出：

[133.3984375, 133.3984375, 133.3984375]

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其次，使用Numpy：

from PIL import Image
import numpy as np

# Create 256x256 image whose rows will have different means depending on the y-value
im = Image.radial_gradient('L').convert('RGB')

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的

# Convert to Numpy array
na = np.array(im)

# print mean of row 0, row 64 and row 128
print(np.mean(na[0]), np.mean(na[64]), np.mean(na[128]))

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打印值：

207.3203125 133.3984375 90.01171875

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如果您希望计算沿沿着 * 任何 * 线（不一定是垂直或水平线）的平均值（或线轮廓），则可以使用scikit-image。

from PIL import Image
import numpy as np
from skimage.draw import line

# Create 256x256 image whose rows will have different means depending on the y-value
im = Image.radial_gradient('L').convert('RGB')

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的

# Convert to Numpy array
na = np.array(im)

# Get row/column coordinates of diagonal line from top-left to bottom-right
rr, cc = line(0,0, 255,255)

# Get means of points along diagonal
result = np.mean(na[rr,cc])

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