matplotlib:渲染到缓冲区/访问像素数据

问题描述

我想将用matplotlib生成的图用作OpenGL中的纹理.到目前为止，我遇到的matplotlib的OpenGL后端不成熟或已停产，所以我想避免使用它们.

I want to use plots generated with matplotlib as textures in OpenGL. The OpenGL backends for matplotlib I came across so far are either immature or discontinued, so I want to avoid them.

我目前的方法是将图形保存到临时的.png文件中，从中组合纹理图集.但是，我宁愿避免存储中间文件，而是直接从matplotlib获取像素数据.这有可能吗?

My current approach is to save figures into temporary .png files from which I assemble texture atlases. However, I would prefer to avoid storing intermediate files and get pixel data directly from matplotlib instead. Is this possible somehow?

我一直在寻找的答案是fig.canvas.print_to_buffer().乔的答案包含其他值得一试的替代方法.

The answer I was looking for is fig.canvas.print_to_buffer(). Joe's answer contains other alternatives worth checking out.

推荐答案

当然，只需使用fig.canvas.tostring_rgb()将rgb缓冲区转储到字符串中即可.

Sure, just use fig.canvas.tostring_rgb() to dump the rgb buffer to a string.

类似地，如果您还需要Alpha通道，则还有fig.canvas.tostring_argb().

Similarly, there's fig.canvas.tostring_argb() if you need the alpha channel, as well.

如果要将缓冲区转储到文件，则有fig.canvas.print_rgb和fig.canvas.print_rgba(或等价的print_raw，即rgba).

If you want to dump the buffer to a file, there's fig.canvas.print_rgb and fig.canvas.print_rgba (or equivalently, print_raw, which is rgba).

在使用tostring*转储缓冲区之前，您需要绘制图形. (即在调用fig.canvas.tostring_rgb()之前先执行fig.canvas.draw())

You'll need to draw the figure before dumping the buffer with tostring*. (i.e. do fig.canvas.draw() before calling fig.canvas.tostring_rgb())

只是为了好玩，这是一个很愚蠢的例子:

Just for fun, here's a rather silly example:

import matplotlib.pyplot as plt
import numpy as np

def main():
    t = np.linspace(0, 4*np.pi, 1000)
    fig1, ax = plt.subplots()
    ax.plot(t, np.cos(t))
    ax.plot(t, np.sin(t))

    inception(inception(fig1))
    plt.show()

def fig2rgb_array(fig):
    fig.canvas.draw()
    buf = fig.canvas.tostring_rgb()
    ncols, nrows = fig.canvas.get_width_height()
    return np.fromstring(buf, dtype=np.uint8).reshape(nrows, ncols, 3)

def inception(fig):
    newfig, ax = plt.subplots()
    ax.imshow(fig2rgb_array(fig))
    return newfig

main()

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