How to decode color mapping in matplotlib Colormap?
I know how to match number to color from this post: Display values in colors in matplotlib
But I don't know how to decode the mapping to get its original color, assuming it is a one-to-one mapping that should be what I'm figuring out.
I am encoding an image for visualization purposes, but I need to decode it and read the original data values.
For reference, here are the Colormap docs: http://matplotlib.org/api/cm_api.html
Here's my attempt at answering the main answer below, which still doesn't work correctly.
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]
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Inverting a collaboration is possible if (a) you know the range of data it displays and
(b) if you know the color palette used, and
(c) if the color map is unambiguous.
The following function returns the value specified by the color, color palette, and range in which the color map was used.
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 contains an error, so you only get the closest value from the color map, not the value that was originally used to generate the color from the map.
In the updated code from the question, you have a color defined as integers from 0 to 255 for each channel. Therefore, you need to display them in the range from 0 to 1 first.
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)
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