OpenCV 算术运算四件套：NOT/AND/OR 位运算与图像混合

OpenCV 算术运算四件套：NOT/AND/OR 位运算与图像混合

OpenCV 图像处理课程 · 第 5 章「Arithmetic Operation」学习笔记

环境：Python 3.10 + opencv-python 4.5.5.64 + PyCharm（chapter5 目录）

包含 4 课：Not / AND / OR 位运算 + 图像混合

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章节总览

课	主题	核心函数	文件
01	非操作 NOT	cv2.bitwise_not()	not_operation.py
02	与操作 AND	cv2.bitwise_and()	and_operation.py
03	或操作 OR	cv2.bitwise_or()	or_operation.py
04	图像混合	cv2.addWeighted()	image_blending.py

本章说明 ：位运算用于图像掩码（masking） ，提取图像关键部分。多个输入图像的尺寸必须相同，输出尺寸与输入一致，逐像素运算。

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第 1 课 · 非操作 NOT（cv2.bitwise_not）

真值表

输入	输出
0	1
1	0

概念

• 常用于二值图像（像素只有 0 和 255 两个值：黑/白）
• cv2.bitwise_not 输出图像的反色：0→255，255→0

函数签名

cv2.bitwise_not(src, dst=None, mask=None)

• src：输入二值图像
• dst / mask：可选，一般传 None（可省略）

完整代码（not_operation.py）

import cv2

image_path = "../data/mask.jpg"
image = cv2.imread(image_path)

not_image = cv2.bitwise_not(image)

cv2.imshow("image", image)
cv2.imshow("not image", not_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

演示结果

• 输入黑色像素（0）→ 输出白色像素（255）
• 输入白色像素（255）→ 输出黑色像素（0）

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cv2.bitwise_not
输入图像（二值）
黑 (0)
白 (255)
逐像素取反
白 (255)
黑 (0)

真实演示图（用遮罩图测试反色）：

输入（二值遮罩）	输出（NOT 反色）

白色(255)区域 → 变黑；黑色(0)区域 → 变白。

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第 2 课 · 与操作 AND（cv2.bitwise_and）

真值表

输入 A	输入 B	输出
0	0	0
0	1	0
1	0	0
1	1	1

只有当两个像素都大于 0 时结果为真。

概念

• 逐像素 AND：任一输入像素为 0，输出就为 0（黑色）
• 用于掩码：保留两图都亮(>0)的区域，遮掉其他

函数签名

cv2.bitwise_and(src1, src2, dst=None, mask=None)

• src1 / src2：两个输入数组/标量
• 两个输入图像形状必须相同，否则报错

完整代码（and_operation.py）

import cv2

image_path1 = "../data/mask.jpg"
image_path2 = "../data/horse.jpg"
image_one = cv2.imread(image_path1)
image_two = cv2.imread(image_path2)

image_and = cv2.bitwise_and(image_one, image_two)

cv2.imshow("image_one", image_one)
cv2.imshow("image_two", image_two)
cv2.imshow("image and", image_and)
cv2.waitKey(0)
cv2.destroyAllWindows()

要点

• 两图交点：亮度>0 的区域保留，否则变黑
• 例：用 mask.jpg（黑白遮罩）对 horse.jpg 做 AND → 只显示马匹区域
• 形状必须一致，否则抛错

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原图 horse
遮罩 mask（黑白）
白色区域

(255, 保留)
黑色区域

(0, 遮掉)
马匹区域
背景区域
只保留白色遮罩覆盖区

= 马匹
背景被遮成黑

真实演示图（遮罩对场景图做 AND 抠取）：

输入1（遮罩）	输入2（场景图）	输出（AND）

白色遮罩区域内的内容（蓝色矩形+红圆）被保留，黑色遮罩区域被遮成黑色。

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第 3 课 · 或操作 OR（cv2.bitwise_or）

真值表

输入 A	输入 B	输出
0	0	0
0	1	1
1	0	1
1	1	1

只要任一个像素大于 0 结果就为真。

概念

• 逐像素 OR：任一像素为 255，输出就为 255（白）
• 用于合并/取消掩码区域：两图像素都 >0 则保留

函数签名

cv2.bitwise_or(src1, src2, dst=None, mask=None)

• src1 / src2 形状必须相同

完整代码（or_operation.py）

import cv2

image_path1 = "../data/mask.jpg"
image_path2 = "../data/horse.jpg"
image_one = cv2.imread(image_path1)
image_two = cv2.imread(image_path2)

image_or = cv2.bitwise_or(image_one, image_two)

cv2.imshow("image_one", image_one)
cv2.imshow("image_two", image_two)
cv2.imshow("image or", image_or)
cv2.waitKey(0)
cv2.destroyAllWindows()

要点

• 两图中任意亮度>0 的区域都被合并保留
• 两图像素都为 0 才是黑色

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图像二
图像一
亮区 (255, 保留)
暗区 (0)
亮区 (255, 保留)
暗区 (0)
任一图亮即保留

= 两图内容合并
都暗才为黑

真实演示图（两图合并）：

输入1	输入2	输出（OR）

两图中任一可见的区域都被合并保留，只有两图都是黑的区域才为黑。

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第 4 课 · 图像混合（cv2.addWeighted）

概念

• 图像混合 ：把两个图像的对应像素值按权重相加，生成新图像
• 用处：让两图内容同时出现在输出里（叠加/透明效果）
• 公式：输出 = alpha × img1 + beta × img2 + gamma

函数签名

cv2.addWeighted(src1, alpha, src2, beta, gamma)

参数	含义
src1 / src2	两个输入图像
alpha	src1 的权重（通常 0~1）
beta	src2 的权重（通常 0~1）
gamma	加到所有像素的静态权值

通常 alpha + beta = 1。两图尺寸必须一致。

完整代码（image_blending.py）

import cv2

image_path1 = "../data/horse.jpg"
image_path2 = "../data/horse1.jpg"

image_one = cv2.imread(image_path1)
image_two = cv2.imread(image_path2)

print("image1 shape:", image_one.shape)   # (360, 540, 3)
print("image2 shape:", image_two.shape)   # 尺寸不同！

# 若两图尺寸不同，先把 img2 缩放到 img1 尺寸
height, width, channels = image_one.shape
image_two = cv2.resize(image_two, (width, height))

blend_image = cv2.addWeighted(image_one, 0.5, image_two, 0.5, 0)

cv2.imshow("image_one", image_one)
cv2.imshow("image_two", image_two)
cv2.imshow("blend image", blend_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

要点

• alpha=0.5, beta=0.5 → 两图各占一半透明叠加
• 调 beta ：beta=0.1 → 图2 很淡；beta=0 → 图2 完全消失
• 尺寸不同要先 resize （用 cv2.resize(image_two, (width, height))），否则抛错

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cv2.addWeighted
两个输入图像（先 resize 对齐尺寸）
image_one

horse.jpg
image_two

horse1.jpg
输出 = α·img1 + β·img2 + γ

(α=0.5, β=0.5, γ=0)
两图各占 50% 半透明叠加

真实演示图（50% 加权混合）：

输入1 (α=0.5)	输入2 (β=0.5)	输出（混合）

两图各贡献 50%，两个场景内容都能看到（半透明叠加）。

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本章小结（速查表）

运算	函数	真值逻辑	用途
NOT	cv2.bitwise_not(src)	0↔1	反色
AND	cv2.bitwise_and(s1, s2)	都>0 才真	掩码/抠局部
OR	cv2.bitwise_or(s1, s2)	任一>0 即真	合并区域
混合	cv2.addWeighted(s1,a,s2,b,γ)	加权相加	图像叠加

核心易错点

1. 位运算输入尺寸必须相同（不同会抛错）
2. 颜色/像素都是 BGR，位运算是逐像素的
3. addWeighted 的 alpha+beta 通常=1，gamma 加在全体像素上
4. 混合前若两图尺寸不同，先 cv2.resize 对齐