python opencv 掩膜筛选

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mob64ca1405a060 2024-11-18 08:45:41

文章标签 python opencv 掩膜筛选 opencv ci 位运算 Line 文章分类 Python 后端开发

3、图像位运算

位运算在图像处理，特别是掩膜中有着极其重要的作用，主要有与、或、异或以及非等操作。

Operation	Function
AND	cv2.bitwise_and
OR	cv2.bitwise_or
XOR	cv2.bitwise_xor
NOT	cv2.bitwise_not

接下来以书上的代码来了解图像的位运算。

a.源代码展现

bitwise.py

import numpy as np
import cv2

# First, let's draw a rectangle
rectangle = np.zeros((300, 300), dtype = "uint8")
cv2.rectangle(rectangle, (25, 25), (275, 275), 255, -1)
cv2.imshow("Rectangle", rectangle)

# Secondly, let's draw a circle
circle = np.zeros((300, 300), dtype = "uint8")
cv2.circle(circle, (150, 150), 150, 255, -1)
cv2.imshow("Circle", circle)

# A bitwise 'AND' is only True when both rectangle
# and circle have a value that is 'ON'. Simply put,
# the bitwise_and function examines every pixel in
# rectangle and circle. If both pixels have a value
# greater than zero, that pixel is turned 'ON' (i.e
# set to 255 in the output image). If both pixels
# are not greater than zero, then the output pixel
# is left 'OFF' with a value of 0.
bitwiseAnd = cv2.bitwise_and(rectangle, circle)
cv2.imshow("AND", bitwiseAnd)
cv2.waitKey(0)

# A bitwise 'OR' examines every pixel in rectangle
# and circle. If EITHER pixel in rectangle or circle
# is greater than zero, then the output pixel has a
# value of 255, otherwise it is 0.
bitwiseOr = cv2.bitwise_or(rectangle, circle)
cv2.imshow("OR", bitwiseOr)
cv2.waitKey(0)

# The bitwise 'XOR' is identical to the 'OR' function,
# with one exception: both rectangle and circle are
# not allowed to have values greater than 0.
bitwiseXor = cv2.bitwise_xor(rectangle, circle)
cv2.imshow("XOR", bitwiseXor)
cv2.waitKey(0)

# Finally, the bitwise 'NOT' inverts the values of
# the pixels. Pixels with a value of 255 become 0,
# and pixels with a value of 0 become 255.
bitwiseNot = cv2.bitwise_not(circle)
cv2.imshow("NOT", bitwiseNot)
cv2.waitKey(0)

b.代码分析

Line 5-7

首先将rectangle定义为一个300*300的黑色画布，然后通过opencv中的cv2.rectangle绘制了一个纯白色的矩形并显示，白色矩形起点和终点分别为(25, 25)和(275, 275)，参数中255表示矩形的像素亮度为255，即纯白，-1表示将矩形内的部分填充，即绘制一个实心矩形。

Line 10-12

同样是在300*300的黑色画布上，绘制一个纯白色的实心圆形并显示，圆形的圆心位置为(150, 150)，半径为150。

Line 22-46

分别对矩形和圆形进行四种位运算，并依次输出这两张图像位运算的结果。

c.结果呈现

python opencv 掩膜筛选_python opencv 掩膜筛选

比较简单，就不对结果做多解释了(^▽)

4、图像掩膜

首先有个疑问：掩膜是啥(⊙_⊙)?

掩膜的本质其实就是图像的位运算，我们通过将不位于感兴趣区域内的像素点与0相与，将像素点的值转变为0，用黑色掩盖住原像素点，进而突出我们感兴趣的图像区域。当我们只关注于图像中的一部分内容而不关心其剩余部分时，可以采用掩膜的方式将那不重要的部分图像盖住。

因此，掩膜常常应用于简单的背景替换。【学会了掩膜，就可以自己编程来替换证件照背景啦O(∩_∩)O~】

例如，对于如下的一张海景图，倘若我们只关心天空和椰子树，而不关注像海滩等其余图像部分，那么我们就可以首先通过绘制一张矩形掩膜，然后通过与原图像的位运算生成掩膜后的图像，应用矩形掩膜的结果如下表所示。


Original	Mask	Masked image

接下来就看看怎么通过代码实现吧。

a.源代码呈现

masking.py

import numpy as np
import argparse
import cv2

# Construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True,
	help = "Path to the image")
args = vars(ap.parse_args())

# Load the image and show it
image = cv2.imread(args["image"])
cv2.imshow("Original", image)

# Masking allows us to focus only on parts of an image that
# interest us. A mask is the same size as our image, but has
# only two pixel values, 0 and 255. Pixels with a value of 0
# are ignored in the orignal image, and mask pixels with a
# value of 255 are allowed to be kept. For example, let's
# construct a mask with a 150x150 square at the center of it
# and mask our image.
mask = np.zeros(image.shape[:2], dtype = "uint8")
(cX, cY) = (image.shape[1] // 2, image.shape[0] // 2)
cv2.rectangle(mask, (cX - 75, cY - 75), (cX + 75 , cY + 75), 255, -1)
cv2.imshow("Mask", mask)

# Apply out mask -- notice how only the center rectangular
# region of the pill is shown
masked = cv2.bitwise_and(image, image, mask = mask)
cv2.imshow("Mask Applied to Image", masked)
cv2.waitKey(0)

# Now, let's make a circular mask with a radius of 100 pixels
mask = np.zeros(image.shape[:2], dtype = "uint8")
cv2.circle(mask, (cX, cY), 100, 255, -1)
masked = cv2.bitwise_and(image, image, mask = mask)
cv2.imshow("Mask", mask)
cv2.imshow("Mask Applied to Image", masked)
cv2.waitKey(0)