blocks|key|488082|text|用Pillow怎么样？|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|488083|from+PIL+import+Image
img+=+Image.open('image.png').convert('L')
img.save('greyscale.png')|code-block|syntax|javascript|488084|488085|如果输入图像中存在alpha+(透明度)通道并应保留该通道，请使用模式LA|style|CODE|488086|img+=+Image.open('image.png').convert('LA')|488087|488088|使用matplotlib和the+formula|488089|Y'+=+0.2989+R+%2B+0.5870+G+%2B+0.1140+B+|488090|你可以这样做：|488091|import+numpy+as+np
import+matplotlib.pyplot+as+plt
import+matplotlib.image+as+mpimg

def+rgb2gray(rgb):
++++return+np.dot(rgb[...,:3],+[0.2989,+0.5870,+0.1140])

img+=+mpimg.imread('image.png')+++++
gray+=+rgb2gray(img)++++
plt.imshow(gray,+cmap=plt.get_cmap('gray'),+vmin=0,+vmax=1)
plt.show()|488092|entityMap|0|LINK|mutability|MUTABLE|url|https://pillow.readthedocs.io/en/latest/|1|https://pillow.readthedocs.io/en/3.2.x/reference/Image.html#PIL.Image.Image.convert^0|1|6|0|0|0|0|Z|2|0|0|0|D|B|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@$A|19|B|1A|1|1B]]|C|$]]|$1|D|3|E|5|F|7|1C|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|1D|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|1E|8|@$A|1F|B|1G|L|M]]|9|@]|C|$]]|$1|N|3|O|5|F|7|1H|8|@]|9|@]|C|$G|H]]|$1|P|3|-4|5|6|7|1I|8|@]|9|@]|C|$]]|$1|Q|3|R|5|6|7|1J|8|@]|9|@$A|1K|B|1L|1|1M]]|C|$]]|$1|S|3|T|5|F|7|1N|8|@]|9|@]|C|$G|H]]|$1|U|3|V|5|6|7|1O|8|@]|9|@]|C|$]]|$1|W|3|X|5|F|7|1P|8|@]|9|@]|C|$G|H]]|$1|Y|3|-4|5|6|7|1Q|8|@]|9|@]|C|$]]]|Z|$10|$5|11|12|13|C|$14|15]]|16|$5|11|12|13|C|$14|17]]]]

How about doing it with <a href="https://pillow.readthedocs.io/en/latest/" rel="noreferrer">Pillow</a>:
<pre><code>from PIL import Image
img = Image.open('image.png').convert('L')
img.save('greyscale.png')
</code></pre>
<hr />
If an alpha (transparency) channel is present in the input image and should be preserved, use mode <code>LA</code>:
<pre><code>img = Image.open('image.png').convert('LA')
</code></pre>
<hr />
Using matplotlib and <a href="https://pillow.readthedocs.io/en/3.2.x/reference/Image.html#PIL.Image.Image.convert" rel="noreferrer">the formula</a>
<pre><code>Y' = 0.2989 R + 0.5870 G + 0.1140 B 
</code></pre>
you could do:
<pre><code>import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def rgb2gray(rgb):
 return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])

img = mpimg.imread('image.png') 
gray = rgb2gray(img) 
plt.imshow(gray, cmap=plt.get_cmap('gray'), vmin=0, vmax=1)
plt.show()
</code></pre>

blocks|key|271029|text|您始终可以使用OpenCV中的imread从一开始就将图像文件作为灰度读取：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|271030|img+=+cv2.imread('messi5.jpg',+0)|code-block|syntax|javascript|271031|此外，如果你想读取图像为RGB，做一些处理，然后转换成灰度，你可以使用OpenCV中的cvtcolor：|271032|gray_image+=+cv2.cvtColor(image,+cv2.COLOR_BGR2GRAY)|271033|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_gui/py_image_display/py_image_display.html|1|http://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html#cvtcolor^0|F|6|F|6|0|0|0|17|8|17|8|1|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]]|D|@$9|11|A|12|1|13]]|E|$]]|$1|F|3|G|5|H|7|14|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|15|8|@$9|16|A|17|B|C]]|D|@$9|18|A|19|1|1A]]|E|$]]|$1|M|3|N|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|1C|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]|W|$5|R|S|T|E|$U|X]]]]

You can always read the image file as grayscale right from the beginning using <a href="http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_gui/py_image_display/py_image_display.html" rel="noreferrer"><code>imread</code></a> from OpenCV:

<pre><code>img = cv2.imread('messi5.jpg', 0)
</code></pre>

Furthermore, in case you want to read the image as RGB, do some processing and then convert to Gray Scale you could use <a href="http://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html#cvtcolor" rel="noreferrer"><code>cvtcolor</code></a> from OpenCV:

<pre><code>gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
</code></pre>

blocks|key|488218|text|最快也是最流行的方式是使用通过pip+install+Pillow安装的Pillow。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|488219|然后代码是：|488220|from+PIL+import+Image
img+=+Image.open('input_file.jpg').convert('L')
img.save('output_file.jpg')|code-block|syntax|javascript|488221|entityMap|0|LINK|mutability|MUTABLE|url|http://pillow.readthedocs.org/en/3.0.x/^0|F|I|10|6|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|F|3|G|5|6|7|10|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|11|8|@]|D|@]|E|$K|L]]|$1|M|3|-4|5|6|7|12|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

The fastest and current way is to use <a href="http://pillow.readthedocs.org/en/3.0.x/" rel="noreferrer">Pillow</a>, installed via <code>pip install Pillow</code>.

The code is then:

<pre><code>from PIL import Image
img = Image.open('input_file.jpg').convert('L')
img.save('output_file.jpg')
</code></pre>

blocks|key|271191|text|image=myCamera.getImage().crop(xx,xx,xx,xx).scale(xx,xx).greyscale()|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|271192|您可以直接使用greyscale()进行转换。|unstyled|offset|length|style|CODE|271193|entityMap^0|0|7|B|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|N|8|@$G|O|H|P|I|J]]|9|@]|A|$]]|$1|K|3|-4|5|F|7|Q|8|@]|9|@]|A|$]]]|L|$]]

<pre><code>image=myCamera.getImage().crop(xx,xx,xx,xx).scale(xx,xx).greyscale()
</code></pre>

You can use <code>greyscale()</code> directly for the transformation.

blocks|key|271242|text|如果你已经在使用NumPy/SciPy，你可以使用as+well+use|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|271243|scipy.ndimage.imread(file_name,+mode='L')|style|CODE|271244|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.imread.html^0|P|B|0|0|0|15|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@$A|U|B|V|F|G]]|9|@]|C|$]]|$1|H|3|-4|5|6|7|W|8|@]|9|@]|C|$]]]|I|$J|$5|K|L|M|C|$N|O]]]]

If you're using NumPy/SciPy already you may <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.imread.html" rel="nofollow noreferrer">as well use</a>:

<code>scipy.ndimage.imread(file_name, mode='L')</code>

blocks|key|271328|text|我通过谷歌来问这个问题，寻找一种将已经加载的图像转换为灰度的方法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|271329|以下是使用SciPy实现这一点的方法：|271330|import+scipy.misc
import+scipy.ndimage

#+Load+an+example+image
#+Use+scipy.ndimage.imread(file_name,+mode='L')+if+you+have+your+own
img+=+scipy.misc.face()

#+Convert+the+image
R+=+img[:,+:,+0]
G+=+img[:,+:,+1]
B+=+img[:,+:,+2]
img_gray+=+R+*+299.+/+1000+%2B+G+*+587.+/+1000+%2B+B+*+114.+/+1000

#+Show+the+image
scipy.misc.imshow(img_gray)|code-block|syntax|javascript|271331|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|L|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|M|8|@]|9|@]|A|$G|H]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

I came to this question via Google, searching for a way to convert an already loaded image to grayscale.

Here is a way to do it with SciPy:

<pre><code>import scipy.misc
import scipy.ndimage

# Load an example image
# Use scipy.ndimage.imread(file_name, mode='L') if you have your own
img = scipy.misc.face()

# Convert the image
R = img[:, :, 0]
G = img[:, :, 1]
B = img[:, :, 2]
img_gray = R * 299. / 1000 + G * 587. / 1000 + B * 114. / 1000

# Show the image
scipy.misc.imshow(img_gray)
</code></pre>

blocks|key|488436|text|你可以这样做：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|488437|import+numpy+as+np
import+matplotlib.pyplot+as+plt
import+matplotlib.image+as+mpimg

def+rgb_to_gray(img):
++++++++grayImage+=+np.zeros(img.shape)
++++++++R+=+np.array(img[:,+:,+0])
++++++++G+=+np.array(img[:,+:,+1])
++++++++B+=+np.array(img[:,+:,+2])

++++++++R+=+(R+*.299)
++++++++G+=+(G+*.587)
++++++++B+=+(B+*.114)

++++++++Avg+=+(R%2BG%2BB)
++++++++grayImage+=+img.copy()

++++++++for+i+in+range(3):
+++++++++++grayImage[:,:,i]+=+Avg
+++++++++++
++++++++return+grayImage+++++++

image+=+mpimg.imread("your_image.png")+++
grayImage+=+rgb_to_gray(image)++
plt.imshow(grayImage)
plt.show()|code-block|syntax|javascript|488438|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

you could do:
<pre><code>import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def rgb_to_gray(img):
 grayImage = np.zeros(img.shape)
 R = np.array(img[:, :, 0])
 G = np.array(img[:, :, 1])
 B = np.array(img[:, :, 2])

 R = (R *.299)
 G = (G *.587)
 B = (B *.114)

 Avg = (R+G+B)
 grayImage = img.copy()

 for i in range(3):
 grayImage[:,:,i] = Avg
 
 return grayImage 

image = mpimg.imread(&quot;your_image.png&quot;) 
grayImage = rgb_to_gray(image) 
plt.imshow(grayImage)
plt.show()
</code></pre>

blocks|key|271374|text|使用img.Convert()，支持“L”、“RGB”和“CMYK”。模式|type|unstyled|depth|inlineStyleRanges|entityRanges|data|271375|import+numpy+as+np
from+PIL+import+Image

img+=+Image.open("IMG/center_2018_02_03_00_34_32_784.jpg")
img.convert('L')

print+np.array(img)|code-block|syntax|javascript|271376|输出：|271377|[[135+123+134+...,++30+++3++14]
+[137+130+137+...,+++9++20++13]
+[170+177+183+...,++14++10+250]
+...,+
+[112++99++91+...,++90++88++80]
+[+95+103+111+...,+102++85+103]
+[112++96++86+...,+182+148+114]]|271378|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Use img.Convert(), supports “L”, “RGB” and “CMYK.” mode

<pre><code>import numpy as np
from PIL import Image

img = Image.open("IMG/center_2018_02_03_00_34_32_784.jpg")
img.convert('L')

print np.array(img)
</code></pre>

Output: 

<pre><code>[[135 123 134 ..., 30 3 14]
 [137 130 137 ..., 9 20 13]
 [170 177 183 ..., 14 10 250]
 ..., 
 [112 99 91 ..., 90 88 80]
 [ 95 103 111 ..., 102 85 103]
 [112 96 86 ..., 182 148 114]]
</code></pre>

blocks|key|488531|text|使用this+formula|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|488532|Y'+=+0.299+R+%2B+0.587+G+%2B+0.114+B+|code-block|syntax|javascript|488533|我们可以做到|488534|import+imageio
import+numpy+as+np
import+matplotlib.pyplot+as+plt

pic+=+imageio.imread('(image)')
gray+=+lambda+rgb+:+np.dot(rgb[...+,+:3]+,+[0.299+,+0.587,+0.114])+
gray+=+gray(pic)++
plt.imshow(gray,+cmap+=+plt.get_cmap(name+=+'gray'))|488535|然而，GIMP将颜色转换为灰度图像的软件有三种算法来完成这项任务。|488536|entityMap|0|LINK|mutability|MUTABLE|url|https://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale|1|https://docs.gimp.org/2.6/en/gimp-tool-desaturate.html^0|2|C|0|0|0|0|0|3|4|1|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]]|C|$]]|$1|D|3|E|5|F|7|12|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|13|8|@]|9|@]|C|$]]|$1|K|3|L|5|F|7|14|8|@]|9|@]|C|$G|H]]|$1|M|3|N|5|6|7|15|8|@]|9|@$A|16|B|17|1|18]]|C|$]]|$1|O|3|-4|5|6|7|19|8|@]|9|@]|C|$]]]|P|$Q|$5|R|S|T|C|$U|V]]|W|$5|R|S|T|C|$U|X]]]]

Using <a href="https://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale" rel="noreferrer">this formula</a> 

<pre><code>Y' = 0.299 R + 0.587 G + 0.114 B 
</code></pre>

We can do

<pre><code>import imageio
import numpy as np
import matplotlib.pyplot as plt

pic = imageio.imread('(image)')
gray = lambda rgb : np.dot(rgb[... , :3] , [0.299 , 0.587, 0.114]) 
gray = gray(pic) 
plt.imshow(gray, cmap = plt.get_cmap(name = 'gray'))
</code></pre>

However, the <a href="https://docs.gimp.org/2.6/en/gimp-tool-desaturate.html" rel="noreferrer">GIMP</a> converting color to grayscale image software has three algorithms to do the task.

blocks|key|271506|text|有了OpenCV，事情变得很简单：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|271507|import+cv2

im+=+cv2.imread("flower.jpg")

#+To+Grayscale
im+=+cv2.cvtColor(im,+cv2.COLOR_BGR2GRAY)
cv2.imwrite("grayscale.jpg",+im)

#+To+Black+&+White
im+=+cv2.threshold(im,+127,+255,+cv2.THRESH_BINARY)[1]
cv2.imwrite("black-white.jpg",+im)|code-block|syntax|javascript|271508|​|271509|📷|atomic|offset|length|271510|271511|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/76511c3fbb24b1732e549a92d0adf9a8.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Z|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|10|8|@]|9|@$L|11|M|12|1|13]]|A|$]]|$1|N|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V|W|-4]]]]

With OpenCV its simple:
<pre><code>import cv2

im = cv2.imread(&quot;flower.jpg&quot;)

# To Grayscale
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
cv2.imwrite(&quot;grayscale.jpg&quot;, im)

# To Black &amp; White
im = cv2.threshold(im, 127, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite(&quot;black-white.jpg&quot;, im)
</code></pre>
<a href="https://i.stack.imgur.com/J4eEv.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/J4eEv.png" alt="enter image description here" /></a>

I'm trying to use <code>matplotlib</code> to read in an RGB image and convert it to grayscale.

In matlab I use this:

<pre><code>img = rgb2gray(imread('image.png'));
</code></pre>

In the <a href="http://matplotlib.sourceforge.net/users/image_tutorial.html" rel="noreferrer">matplotlib tutorial</a> they don't cover it. They just read in the image

<pre><code>import matplotlib.image as mpimg
img = mpimg.imread('image.png')
</code></pre>

and then they slice the array, but that's not the same thing as converting RGB to grayscale from what I understand.

<pre><code>lum_img = img[:,:,0]
</code></pre>

I find it hard to believe that numpy or matplotlib doesn't have a built-in function to convert from rgb to gray. Isn't this a common operation in image processing?

I wrote a very simple function that works with the image imported using <code>imread</code> in 5 minutes. It's horribly inefficient, but that's why I was hoping for a professional implementation built-in. 

Sebastian has improved my function, but I'm still hoping to find the built-in one.

matlab's (NTSC/PAL) implementation:

<pre><code>import numpy as np

def rgb2gray(rgb):

 r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
 gray = 0.2989 * r + 0.5870 * g + 0.1140 * b

 return gray
</code></pre>

How can I convert an RGB image into grayscale in Python?

图像处理

我正在尝试使用matplotlib读取RGB图像并将其转换为灰度。在matlab中，我使用的是：img = rgb2gray(imread('image.png'));在中，他们不会覆盖它。他们只是在图像中读出import matplotlib.image as mpimgimg = mpimg.imread('ima...

问如何在Python中将RGB图像转换为灰度图像？
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