blocks|key|4248360|text|假设没有alpha带，不是就这么简单吗？|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4248361|b,+g,+r+=+im.split()
im+=+Image.merge("RGB",+(r,+g,+b))|code-block|syntax|javascript|4248362|编辑：|4248363|嗯..。似乎PIL在这方面有一些bug...im.split()似乎不适用于最新版本的PIL+(1.1.7)。它可能(?)不过，仍然可以使用1.1.6。|offset|length|style|CODE|4248364|entityMap^0|0|0|0|M|A|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|T|8|@$K|U|L|V|M|N]]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

Assuming no alpha band, isn't it as simple as this?

<pre><code>b, g, r = im.split()
im = Image.merge("RGB", (r, g, b))
</code></pre>

Edit:

Hmm... It seems PIL has a few bugs in this regard... <code>im.split()</code> doesn't seem to work with recent versions of PIL (1.1.7). It may (?) still work with 1.1.6, though...

blocks|key|4248478|text|我知道这是一个古老的问题，但我也有同样的问题，并用以下方法解决了它：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4248479|img+=+img[:,:,::-1]|code-block|syntax|javascript|4248480|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|$]]

I know it's an old question, but I had the same problem and solved it with:

<pre><code>img = img[:,:,::-1]
</code></pre>

blocks|key|4248419|text|只是添加一个更新的答案：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4248420|有了新的cv2界面，图像现在可以自动加载到numpy数组中。|4248421|但是openCV+cv2.imread()将图像加载为BGR，而numpy.imread()将它们加载为RGB。|4248422|最简单的转换方法是使用openCV+cvtColor。|4248423|import+cv2
srcBGR+=+cv2.imread("sample.png")
destRGB+=+cv2.cvtColor(srcBGR,+cv2.COLOR_BGR2RGB)|code-block|syntax|javascript|4248424|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|Q|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|R|8|@]|9|@]|A|$]]|$1|H|3|I|5|J|7|S|8|@]|9|@]|A|$K|L]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

Just to add a more up to date answer:

With the new cv2 interface images loaded are now numpy arrays automatically. 
But openCV cv2.imread() loads images as BGR while numpy.imread() loads them as RGB.

The easiest way to convert is to use openCV cvtColor.

<pre><code>import cv2
srcBGR = cv2.imread("sample.png")
destRGB = cv2.cvtColor(srcBGR, cv2.COLOR_BGR2RGB)
</code></pre>

blocks|key|4248512|text|使用省略号添加解决方案|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4248513|image+=+image[...,::-1]|offset|length|style|CODE|4248514|在本例中，省略号通道等同于:,:，而::-1颠倒了最后一个维度(+...+)的顺序。|4248515|entityMap^0|0|0|N|0|D|3|I|4|X|3|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|M|8|@$D|N|E|O|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|P|8|@$D|Q|E|R|F|G]|$D|S|E|T|F|G]|$D|U|E|V|F|G]]|9|@]|A|$]]|$1|J|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|K|$]]

Adding a solution using the ellipsis

<code>image = image[...,::-1]</code>

In this case, the ellipsis <code>...</code> is equivalent to <code>:,:</code> while <code>::-1</code> inverts the order of the last dimension (channels).

blocks|key|4248397|text|这是我最好的回答。顺便说一句，这也适用于Alpha。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4248398|from+PIL+import+Image
import+numpy+as+np
import+sys+

sub+=+Image.open(sys.argv[1])
sub+=+sub.convert("RGBA")
data+=+np.array(sub)+
red,+green,+blue,+alpha+=+data.T+
data+=+np.array([blue,+green,+red,+alpha])
data+=+data.transpose()
sub+=+Image.fromarray(data)|code-block|syntax|javascript|4248399|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|$]]

This was my best answer. This does, by the way, work with Alpha too.

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

sub = Image.open(sys.argv[1])
sub = sub.convert("RGBA")
data = np.array(sub) 
red, green, blue, alpha = data.T 
data = np.array([blue, green, red, alpha])
data = data.transpose()
sub = Image.fromarray(data)
</code></pre>

blocks|key|2645645|text|import+cv2
srcBGR+=+cv2.imread("sample.png")
destRGB+=+cv2.cvtColor(srcBGR,cv2.COLOR_BGR2RGB)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|2645646|只是为了澄清马丁·贝克尔斯的解决方案，因为我无法置评。你需要cv2。在颜色常量前面。|unstyled|2645647|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|J|8|@]|9|@]|A|$]]|$1|G|3|-4|5|F|7|K|8|@]|9|@]|A|$]]]|H|$]]

<pre><code>import cv2
srcBGR = cv2.imread("sample.png")
destRGB = cv2.cvtColor(srcBGR,cv2.COLOR_BGR2RGB)
</code></pre>

Just to clarify Martin Beckets solution, as I am unable to comment. 
You need cv2. in front of the color constant.

blocks|key|4248565|text|对于任何编写可能需要处理4通道图像的代码的人来说，这只是一个快速的脚注，并且发现简单的麻木答案似乎正在吞噬他们的alpha通道。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4248566|np_image[:,:,[0,1,2]]+=+np_image[:,:,[2,1,0]]|code-block|syntax|javascript|4248567|如果有第四个通道，将保留alpha数据，而|4248568|np_image+=+np_image[:,:,[2,1,0]]|4248569|将仅用反转的3通道数据覆盖4通道图像。(更简单的numpy答案是img+=+img:，:，::-1，它会给出ARGB数据，这也很糟糕。:)|4248570|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

Just a quick footnote for anyone writing code that might have to deal with 4-channel images, and discovering that the simple numpy answer seems to be eating their alpha channel.
<pre><code>np_image[:,:,[0,1,2]] = np_image[:,:,[2,1,0]]
</code></pre>
will preserve the alpha data if there is a fourth channel, whereas
<pre><code>np_image = np_image[:,:,[2,1,0]]
</code></pre>
will overwrite the 4-channel image with only reversed 3-channel data. (And the even simpler numpy answer, img = img[:,:,::-1], will give you ARGB data, which would be bad, too. :)

blocks|key|1641246|text|im+=+Image.frombuffer('RGB',+(width,+height),+bgr_buf,+'raw',+'BGR',+0,+0)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|1641247|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>im = Image.frombuffer('RGB', (width, height), bgr_buf, 'raw', 'BGR', 0, 0)
</code></pre>

blocks|key|579115|text|使用前面解释的想法...使用numpy你可以。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|579116|bgr_image_array+=+numpy.asarray(bgr_image)
B,+G,+R+=+bgr_image_array.T
rgb_image_array+=+np.array((R,+G,+B)).T
rgb_image+=+Image.fromarray(rgb_image_array,+mode='RGB')|code-block|syntax|javascript|579117|此外，它还可以移除Alpha通道。|579118|assert+bgra_image_array.shape+==+(image_height,+image_width,+4)
B,+G,+R,+_+=+bgra_image_array.T
rgb_image_array+=+np.array((R,+G,+B)).T|579119|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|$]]

Using the ideas explained before... using numpy you could.

<pre><code>bgr_image_array = numpy.asarray(bgr_image)
B, G, R = bgr_image_array.T
rgb_image_array = np.array((R, G, B)).T
rgb_image = Image.fromarray(rgb_image_array, mode='RGB')
</code></pre>

Additionally it can remove the Alpha channel.

<pre><code>assert bgra_image_array.shape == (image_height, image_width, 4)
B, G, R, _ = bgra_image_array.T
rgb_image_array = np.array((R, G, B)).T
</code></pre>

blocks|key|579188|text|其他解决方案的应用。只是暂时的措施。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|579189|import+numpy

im+=+Image.fromarray(numpy.array(im)[:,:,::-1])|code-block|syntax|javascript|579190|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|$]]

Application of other solutions. Just for a temporary measure.
<pre class="lang-py prettyprint-override"><code>import numpy

im = Image.fromarray(numpy.array(im)[:,:,::-1])
</code></pre>

blocks|key|4248315|text|您应该能够使用ImageMath模块做到这一点。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4248316|编辑：|4248317|Joe的解决方案更好，我想得太多了。:)|4248318|entityMap|0|LINK|mutability|MUTABLE|url|http://effbot.org/imagingbook/imagemath.htm^0|7|9|7|9|0|0|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@$9|S|A|T|B|C]]|D|@$9|U|A|V|1|W]]|E|$]]|$1|F|3|G|5|6|7|X|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|Y|8|@]|D|@]|E|$]]|$1|J|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|K|$L|$5|M|N|O|E|$P|Q]]]]

You should be able to do this with the <a href="http://effbot.org/imagingbook/imagemath.htm" rel="nofollow"><code>ImageMath</code></a> module.

<h3>Edit:</h3>

Joe's solution is even better, I was overthinking it. :)

blocks|key|579268|text|TLDR：如果已经导入cv2，则使用cv2.cvtColor(img,+cv2.COLOR_BGR2RGB))。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|CODE|entityRanges|data|579269|579270|速度比较：|579271|%25%25timeit
img_+=+Image.fromarray(img[...,::-1])
#+5.77+ms+±+12.9+µs+per+loop+(mean+±+std.+dev.+of+7+runs,+100+loops+each)

%25%25timeit
img_+=+Image.fromarray(img[...,[2,1,0]])
#+6.2+ms+±+2.43+µs+per+loop+(mean+±+std.+dev.+of+7+runs,+100+loops+each)

%25%25timeit
img_+=+Image.fromarray(cv2.cvtColor(img,+cv2.COLOR_BGR2RGB))
#+442+µs+±+4.84+µs+per+loop+(mean+±+std.+dev.+of+7+runs,+1000+loops+each)|code-block|syntax|javascript|579272|579273|问题是，OP询问img是否已经是PIL图像格式，而cv2.cvtColor(img,+cv2.COLOR_BGR2RGB))需要numpy数组格式的img。|579274|但是，cv2.imread()很可能是你获得BGR镜像的原因。不是Image.open()。|579275|entityMap^0|0|4|B|3|I|11|0|0|0|0|0|8|3|G|3|P|11|1S|5|22|3|0|3|C|X|C|0^^$0|@$1|2|3|4|5|6|7|V|8|@$9|W|A|X|B|C]|$9|Y|A|Z|B|D]|$9|10|A|11|B|D]]|E|@]|F|$]]|$1|G|3|-4|5|6|7|12|8|@]|E|@]|F|$]]|$1|H|3|I|5|6|7|13|8|@]|E|@]|F|$]]|$1|J|3|K|5|L|7|14|8|@]|E|@]|F|$M|N]]|$1|O|3|-4|5|6|7|15|8|@]|E|@]|F|$]]|$1|P|3|Q|5|6|7|16|8|@$9|17|A|18|B|D]|$9|19|A|1A|B|D]|$9|1B|A|1C|B|D]|$9|1D|A|1E|B|D]|$9|1F|A|1G|B|D]]|E|@]|F|$]]|$1|R|3|S|5|6|7|1H|8|@$9|1I|A|1J|B|D]|$9|1K|A|1L|B|D]]|E|@]|F|$]]|$1|T|3|-4|5|6|7|1M|8|@]|E|@]|F|$]]]|U|$]]

TLDR: Use <code>cv2.cvtColor(img, cv2.COLOR_BGR2RGB))</code> if you already import <code>cv2</code>.
<hr />
Speed comparison:
<pre><code>%%timeit
img_ = Image.fromarray(img[...,::-1])
# 5.77 ms ± 12.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%%timeit
img_ = Image.fromarray(img[...,[2,1,0]])
# 6.2 ms ± 2.43 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%%timeit
img_ = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
# 442 µs ± 4.84 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
</code></pre>
<hr />
Problem is, OP ask if <code>img</code> is already in <code>PIL</code> image format, whereas <code>cv2.cvtColor(img, cv2.COLOR_BGR2RGB))</code> require <code>img</code> in <code>numpy</code> array format.
But, <code>cv2.imread()</code> is most likely the reason you got BGR image. Not <code>Image.open()</code>.

I have an image where the colors are BGR. How can I transform my PIL image to swap the B and R elements of each pixel in an efficient manner?

PIL rotate image colors (BGR -> RGB)

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我有一个图像，其中的颜色是BGR。如何将PIL图像转换为有效地交换每个像素的B和R元素？

问PIL旋转图像颜色(BGR -> RGB)
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回答 12

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