I know how to map a number to a color from this post: Map values to colors in matplotlib

我从这篇文章中知道了如何将数字映射到颜色：在matplotlib中将值映射到颜色

But I don't know how to decode the mapping to get my original color, assuming this is a one-to-one mapping, which it must be I figure.

但我不知道如何解码贴图以获得我的原始颜色，假设这是一对一的贴图，我想这一定是一对一的贴图。

I am encoding an image for visualization purposes, but I need to be able to decode it and read the original data values.

出于可视化目的，我正在对图像进行编码，但我需要能够对其进行解码并读取原始数据值。

For reference, here are the Colormap docs: http://matplotlib.org/api/cm_api.html

以下是Colormap文档以供参考：http://matplotlib.org/api/cm_api.html

Here's my try to the main answer below, which still isn't working right.

以下是我对以下主要答案的尝试，它仍然不能正常工作。

from PIL import Image
import numpy as np
import matplotlib
import matplotlib.cm as cm
values = [670, 894, 582, 103, 786, 348, 972, 718, 356, 692]
minima = 103
maxima = 972
norm = matplotlib.colors.Normalize(vmin=minima, vmax=maxima, clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=cm.gist_rainbow_r)
c = []
for i in range(10):
    c.append(mapper.to_rgba(values[i], bytes=True))
print(c) # [(75, 255, 0, 255), (255, 77, 0, 255), (0, 255, 64, 255), (255, 0, 191, 255), (255, 250, 0, 255), (0, 72, 255, 255), (255, 0, 40, 255), (151, 255, 0, 255), (0, 83, 255, 255), (108, 255, 0, 255)]

def get_value_from_cm(color, cmap, colrange):
    # color = matplotlib.colors.to_rgba(color)
    r = np.linspace(colrange[0], colrange[1], 10) # there are 10 values
    norm = matplotlib.colors.Normalize(colrange[0], colrange[1])
    mapvals = cmap(norm(r))[:, :4] # there are 4 channels: r,g,b,a
    distance = np.sum((mapvals - color) ** 2, axis=1)
    return r[np.argmin(distance)]

decoded_colors = []
for i in range(10):
    decoded_colors.append(get_value_from_cm(c[i], cm.gist_rainbow_r, colrange=[minima, maxima]))
print(decoded_colors) # [778.88888888888891, 778.88888888888891, 489.22222222222223, 103.0, 778.88888888888891, 392.66666666666669, 103.0, 778.88888888888891, 392.66666666666669, 778.88888888888891]

Inverting the colormapping is possible, if

(a) you know the data range it is mapping and

(b) if you know the colormap that has been used, and

(c) if the colormap is unambiguous.

如果(A)您知道它正在映射的数据范围，(B)如果您知道已使用的色彩映射表，(C)如果色彩映射表是明确的，则可以反转色彩映射。

The following function would return the value given a color, a colormap and the range over which the colormap has been used.

以下函数将在给定颜色、色彩映射表和使用色彩映射表的范围的情况下返回值。

import numpy as np
import matplotlib.colors
import matplotlib.pyplot as plt

def get_value_from_cm(color, cmap, colrange=[0.,1.]):
    color=matplotlib.colors.to_rgb(color)
    r = np.linspace(colrange[0],colrange[1], 256)
    norm = matplotlib.colors.Normalize(colrange[0],colrange[1])
    mapvals = cmap(norm(r))[:,:3]
    distance = np.sum((mapvals - color)**2, axis=1)
    return r[np.argmin(distance)]


b = get_value_from_cm(plt.cm.coolwarm(0.5), plt.cm.coolwarm, [0.,1.])
c = get_value_from_cm(np.array([1,0,0]), plt.cm.coolwarm)

print b                   # 0.501960784314
print plt.cm.coolwarm(b)
# (0.86742763508627452, 0.86437659977254899, 0.86260246201960789, 1.0)
print plt.cm.coolwarm(0.5)
#(0.86742763508627452, 0.86437659977254899, 0.86260246201960789, 1.0)

Note that this method involves an error, so you only get the closest value from the colormap and not the value that has initially been used to create the color from the map.

请注意，此方法包含错误，因此您只能从色彩映射表中获得最接近的值，而不是最初用于从映射表中创建颜色的值。

In the updated code from the question, you have the color defined as integers between 0 and 255 for each channel. You therefore need to first map those to the range 0 to 1.

在问题的更新代码中，您将每个通道的颜色定义为0到255之间的整数。因此，您需要首先将它们映射到0到1的范围。

from PIL import Image
import numpy as np
import matplotlib
import matplotlib.cm as cm
values = [670, 894, 582, 103, 786, 348, 972, 718, 356, 692]
minima = 103
maxima = 972
norm = matplotlib.colors.Normalize(vmin=minima, vmax=maxima, clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=cm.gist_rainbow_r)
c = []
for i in range(10):
    c.append(mapper.to_rgba(values[i], bytes=True))
print(c) # [(75, 255, 0, 255), (255, 77, 0, 255), (0, 255, 64, 255), (255, 0, 191, 255), (255, 250, 0, 255), (0, 72, 255, 255), (255, 0, 40, 255), (151, 255, 0, 255), (0, 83, 255, 255), (108, 255, 0, 255)]

def get_value_from_cm(color, cmap, colrange):
    color = np.array(color)/255. 
    r = np.linspace(colrange[0], colrange[1], 256) 
    norm = matplotlib.colors.Normalize(colrange[0], colrange[1])
    mapvals = cmap(norm(r))[:, :4] # there are 4 channels: r,g,b,a
    distance = np.sum((mapvals - color) ** 2, axis=1)
    return r[np.argmin(distance)]

decoded_colors = []
for i in range(10):
    decoded_colors.append(get_value_from_cm(c[i], cm.gist_rainbow_r, colrange=[minima, maxima]))
print(decoded_colors)