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ilikeyml
OpenCVWithPythonByExample
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<!DOCTYPE html
>
<
html
>
<
head
>
<
meta
charset
='
utf-8
'
>
<
meta
http-equiv
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X-UA-Compatible
"
content
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chrome=1
"
>
<
meta
name
="
description
"
content
="
Opencvwithpythonbyexample : Simple demos using OpenCV for Python
"
>
<
link
rel
="
stylesheet
"
type
="
text/css
"
media
="
screen
"
href
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stylesheets/stylesheet.css
"
>
<
title
>
OpenCV With Python By Example
</
title
>
</
head
>
<
body
>
<!-- HEADER -->
<
div
id
="
header_wrap
"
class
="
outer
"
>
<
header
class
="
inner
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>
<
a
id
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forkme_banner
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href
="
https://github.com/ilikeyml/OpenCVWithPythonByExample
"
>
View on GitHub
</
a
>
<
a
href
="
http://ilikeyml.github.io/OpenCVWithPythonByExample/
"
>
<
h1
id
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project_title
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>
OpenCV With Python By Example
</
h1
>
</
a
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<
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id
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project_tagline
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Simple demos using OpenCV for Python
</
h2
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</
header
>
</
div
>
<!-- MAIN CONTENT -->
<
div
id
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main_content_wrap
"
class
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outer
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<
section
id
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main_content
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class
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<
h2
>
图像色彩空间
</
h2
>
<
h3
>
1.RGB
</
h3
>
<
p
>
RGB是最常用的色彩空间，将一副图像表示为R、G、B三个通道分量合成后的结果
</
p
>
<
h3
>
2.YUV
</
h3
>
<
p
>
YUV将图像分为亮度Y以及色差U、V三个信号。其中“Y”表示明亮度（Luminance或Luma），也就是灰阶值；而“U”和“V” 表示的则是色度（Chrominance或Chroma），作用是描述影像色彩及饱和度，用于指定像素的颜色。“亮度”是透过RGB输入信号来建立的，方法是将RGB信号的特定部分叠加到一起。“色度”则定义了颜色的两个方面─色调与饱和度，分别用Cr和Cb来表示。其中，Cr反映了RGB输入信号红色部分与RGB信号亮度值之间的差异。而Cb反映的是RGB输入信号蓝色部分与RGB信号亮度值之间的差异。
</
p
>
<
h3
>
3.HSV
</
h3
>
<
p
>
HSV(Hue, Saturation, Value)是根据颜色的直观特性由A. R. Smith在1978年创建的一种颜色空间, 也称六角锥体模型(Hexcone Model)。
<
br
>
这个模型中颜色的参数分别是：色调（H），饱和度（S），亮度（V）。
<
br
>
<
strong
>
色调H
</
strong
>
<
br
>
用角度度量，取值范围为0°～360°，从红色开始按逆时针方向计算，红色为0°，绿色为120°,蓝色为240°。它们的补色是：黄色为60°，青色为180°,品红为300°。
<
br
>
<
strong
>
饱和度S
</
strong
>
<
br
>
取值范围为0.0～1.0，值越大，颜色越饱和。
<
br
>
<
strong
>
亮度V
</
strong
>
<
br
>
亮度V通常用百分比度量，从0%（黑）到100%（白）。
RGB和CMY颜色模型都是面向硬件的，而HSV（Hue Saturation Value）颜色模型是面向用户的。
HSV模型的三维表示从RGB立方体演化而来。设想从RGB沿立方体对角线的白色顶点向黑色顶点观察，就可以看到立方体的六边形外形。六边形边界表示色彩，水平轴表示纯度，明度沿垂直轴测量。
<
img
src
="
accessories/HSV.jpg
"
alt
="
Color Space
"
>
</
p
>
<
p
>
<
strong
>
三个图像空间可以通过OpenCV自带的函数进行转换
</
strong
>
<
br
>
数字图像处理中通常会对彩色图像进行分通道处理
</
p
>
<
p
>
1.读取一副图像
<
br
>
<
code
>
import cv2 as cv
</
code
>
<
br
>
<
code
>
img = cv.imread("../ImageSource/redSample.jpg")
</
code
>
<
br
>
<
code
>
cv.imshow("in", img)
</
code
>
<
br
>
<
code
>
print img.shape
</
code
>
<
br
>
调用
<
em
>
print img.shape
</
em
>
我们可以得到
<
em
>
img
</
em
>
的数据结构
<
br
>
<
code
>
(196, 279, 3)
</
code
>
<
br
>
最后一位“3”表示
<
em
>
img
</
em
>
的三个通道
<
br
>
OpenCV默认的通道顺序为B-G-R
<
br
>
<
code
>
B, G, R = cv.split(img)
</
code
>
<
br
>
<
code
>
cv.imshow("B channel", B)
</
code
>
<
br
>
<
code
>
cv.imshow("G channel", G)
</
code
>
<
br
>
<
code
>
cv.imshow("R channel", R)
</
code
>
<
br
>
<
img
src
="
accessories/BGRSplit.png
"
alt
="
BGRSplit
"
>
<
br
>
<
strong
>
色彩空间转换
</
strong
>
</
p
>
<
p
>
<
code
>
#BGR2YUV 转换
</
code
>
<
br
>
<
code
>
img_YUVtrans = cv.cvtColor(img,cv.COLOR_BGR2YUV)
</
code
>
<
br
>
<
code
>
cv.imshow("YUV mode", img_YUVtrans)
</
code
>
<
br
>
<
code
>
#BGR2HSV 转换
</
code
>
<
br
>
<
code
>
img_HSVtrans = cv.cvtColor(img, cv.COLOR_BGR2HSV)
</
code
>
<
br
>
<
code
>
cv.imshow("HSV mode", img_HSVtrans)
</
code
>
<
br
>
<
img
src
="
accessories/colorSpace.png
"
alt
="
ColorSpace
"
>
</
p
>
</
section
>
</
div
>
<!-- FOOTER -->
<
div
id
="
footer_wrap
"
class
="
outer
"
>
<
footer
class
="
inner
"
>
<
p
class
="
copyright
"
>
OpenCV with Python by Example maintained by
<
a
href
="
https://github.com/ilikeyml
"
>
ilikeyml
</
a
>
</
p
>
<
p
>
Published with
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a
href
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GitHub Pages
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