Scipy.misc.imread flatten 参数——转换为灰度 [英] Scipy.misc.imread flatten argument -- converting to grey scale

问题描述

我正在尝试了解此方法如何以灰度转换图像(如果它使用简单均值或加权均值)——我必须参考此方法.

I'm trying to understand how this method transform an image in grey scale (if it uses a simple mean or a weighted mean) -- I have to reference this method.

从 文档 我知道此方法调用 convert('F') 方法.

From the documentation I know that this method calls the convert(‘F’) method.

来自 Pillow/PIL 源代码，我可以找到这个方法，但是，当mode参数设置为'F'时，我找不到它的作用.

From Pillow/PIL source code, I can find this method, however, I couldn't find what it does when the mode parameter is set to 'F'.

谢谢.

推荐答案

在 Image 对象的 convert 方法的文档字符串中(在你链接的代码中看到到)，有这个:

In the docstring of the convert method of the Image object (seen in the code that you linked to), there is this:

将彩色图像转换为黑白(模式L")时，该库使用 ITU-R 601-2 亮度变换::

When translating a color image to black and white (mode "L"), the library uses the ITU-R 601-2 luma transform::

L = R * 299/1000 + G * 587/1000 + B * 114/1000

L = R * 299/1000 + G * 587/1000 + B * 114/1000

显然这也是 mode='F' 的处理方式.

Apparently this is also how mode='F' is handled.

这是一个例子:

In [2]: from PIL import Image

为演示创建一个数组:

In [3]: x = np.random.randint(0, 9, size=(4, 4, 3)).astype(np.uint8)

使用 convert 方法中的 mode='F' 通过转换将数组发送到图像并返回到数组:

Send the array through the conversion to an Image and back to an array, using mode='F' in the convert method:

In [4]: np.array(Image.fromarray(x).convert('F'))
Out[4]: 
array([[ 3.24499989,  6.30499983,  1.86899996,  4.54400015],
       [ 3.54399991,  5.04300022,  4.63000011,  0.29899999],
       [ 2.0539999 ,  3.29900002,  1.85800004,  1.76100004],
       [ 3.9289999 ,  4.76100016,  5.76100016,  2.47799993]], dtype=float32)

将 x 乘以 convert 的文档字符串中显示的因子:

Multiply x by the factors shown in the docstring of convert:

In [5]: f = np.array([0.299, 0.587, 0.114])

In [6]: x.dot(f)
Out[6]: 
array([[ 3.245,  6.305,  1.869,  4.544],
       [ 3.544,  5.043,  4.63 ,  0.299],
       [ 2.054,  3.299,  1.858,  1.761],
       [ 3.929,  4.761,  5.761,  2.478]])

如果我们转换为 np.float32，我们会看到与 convert 方法创建的值完全相同:

If we convert to np.float32, we see exactly the same values as created by the convert method:

In [7]: x.dot(f).astype(np.float32)
Out[7]: 
array([[ 3.24499989,  6.30499983,  1.86899996,  4.54400015],
       [ 3.54399991,  5.04300022,  4.63000011,  0.29899999],
       [ 2.0539999 ,  3.29900002,  1.85800004,  1.76100004],
       [ 3.9289999 ,  4.76100016,  5.76100016,  2.47799993]], dtype=float32)

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