图像的颜色直方图表征了图像的明暗程度，其中，X 轴从左到右依次表示从暗到亮（0-255），最左边为最暗的纯黑色（0），最右边为最亮的纯白色（255）；Y轴表示像素数量.

根据直方图，可以大致估计照片的曝光情况，如果数据主要集中在左边，图像可能是欠曝的；而集中在左边，则可能是过曝. 如果数据分布均匀，大多集中于中间地带，说明照片的色调多为中间调，不会太暗，也不会太亮.

https://www.zhihu.com/zvideo/1464534721841106944

Python 实现

Python 实现如：

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

#
image = cv2.imread("test.jpg")

'''
灰度直方图
'''
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
plt.figure()
plt.title("Grayscale Histogram")
plt.xlabel("Bins")
plt.ylabel("# of Pixels")
plt.plot(hist)
plt.xlim([0, 256])
plt.show()

'''
r、g、b 通道直方图
'''
chans = cv2.split(image)
colors = ("b", "g", "r")
plt.figure()
plt.title("RGB Color Histogram")
plt.xlabel("Bins")
plt.ylabel("# of Pixels")

features = []
for (chan, color) in zip(chans, colors):
    # 对各通道计算直方图
    hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
    features.extend(hist)

    plt.plot(hist, color = color)
    plt.xlim([0, 256])
plt.show()
print("flattened feature vector size: %d" % (np.array(features).flatten().shape))

'''
2D 直方图
'''
fig = plt.figure()
plt.title("RGB Color Histogram")

# 2D 颜色直方图： green and blue
ax = fig.add_subplot(131)
hist = cv2.calcHist([chans[1], chans[0]], [0, 1], None,
    [32, 32], [0, 256, 0, 256]) #256 -> 32 bins
p = ax.imshow(hist, interpolation = "nearest")
ax.set_title("Green and Blue")
plt.colorbar(p)

# 2D 颜色直方图： green and red
ax = fig.add_subplot(132)
hist = cv2.calcHist([chans[1], chans[2]], [0, 1], None,
    [32, 32], [0, 256, 0, 256])
p = ax.imshow(hist, interpolation = "nearest")
ax.set_title("Green and Red")
plt.colorbar(p)

# 2D 颜色直方图： blue and red
ax = fig.add_subplot(133)
hist = cv2.calcHist([chans[0], chans[2]], [0, 1], None,
    [32, 32], [0, 256, 0, 256])
p = ax.imshow(hist, interpolation = "nearest")
ax.set_title("Blue and Red")
plt.colorbar(p)
plt.show()
print ("2D histogram shape: %s, with %d values" % (
    hist.shape, hist.flatten().shape[0]))


'''
3D 直方图
'''
hist = cv2.calcHist([image], [0, 1, 2],
    None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
print ("3D histogram shape: %s, with %d values" % (
    hist.shape, hist.flatten().shape[0]))
plt.show()

如：

参考

[1] - 今天来讲讲：直方图 - 知乎

[2] - 直方图的高度有什么意义？- 知乎

[3] - 如何看懂照片的直方图？- 知乎