Why can't I convert my RGB values to Munsell colours? #1331
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GeologyAMG
asked this question in
Q&A
Replies: 1 comment 7 replies
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Hello @GeologyAMG, It is hard to understand what is going on given there is no traceback but a cursory look at your CSV file shows that # /// script
# dependencies = [
# "colour-science",
# "matplotlib",
# "pandas",
# ]
# ///
import colour
import pandas as pd
df = pd.read_csv("/Users/kelsolaar/Downloads/SO-05_Full_Res_Edit.csv")
RGB = colour.utilities.tstack([df["R"].values, df["G"].values, df["B"].values]) / 255
XYZ = colour.sRGB_to_XYZ(RGB)
non_representable = []
for i, xyY in enumerate(colour.XYZ_to_xyY(XYZ)):
try:
colour.xyY_to_munsell_colour(xyY)
except AssertionError as exception:
print(f"Error at row {i}: {RGB[i]}, {exception}")
non_representable.append(i)
swatches = [
colour.plotting.ColourSwatch(swatch, "x" if i in non_representable else "")
for i, swatch in enumerate(RGB)
]
colour.plotting.plot_multi_colour_swatches(swatches, columns=15)
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7 replies
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please forgive me if i am being daft, but no one mentioned the actual error thrown:
is this not meaningful? |
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It does not have to be an exact match but it must sit exactly within the volume of the Munsell Renotation system. |
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@KelSolaar but shouldn't chroma be allowed to go to 0? That's what most of these errors are. I think there's either something else going on here that's a bug, |
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The logic is as follows:
>>> for hvc in colour.MUNSELL_COLOURS["ALL"]:
... if hvc[0][-1] < 2:
... print(hvc)
...
>>> We could try relaxing the constraint and see what happens. |
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I just pushed a relaxed constraint between 0 and 50 in develop, and with the above test case, all the colours get converted, max Delta E is 0.6, so not too bad! import colour
import pandas as pd
import numpy as np
df = pd.read_csv("/Users/kelsolaar/Downloads/SO-05_Full_Res_Edit.csv")
RGB = colour.utilities.tstack([df["R"].values, df["G"].values, df["B"].values]) / 255
XYZ = colour.sRGB_to_XYZ(RGB)
non_representable = []
delta_E = []
for i, xyY in enumerate(colour.XYZ_to_xyY(XYZ)):
munsell_colour = None
try:
munsell_colour = colour.xyY_to_munsell_colour(xyY)
except AssertionError as exception:
print(f"Error at row {i}: {RGB[i]}, {exception}")
non_representable.append(i)
if munsell_colour:
delta_E.append(
colour.delta_E(
colour.XYZ_to_Lab(colour.xyY_to_XYZ(xyY)),
colour.XYZ_to_Lab(
colour.xyY_to_XYZ(colour.munsell_colour_to_xyY(munsell_colour))
),
)
)
swatches = [
colour.plotting.ColourSwatch(swatch, "x" if i in non_representable else "")
for i, swatch in enumerate(RGB)
]
colour.plotting.plot_multi_colour_swatches(swatches, columns=15)
print(np.min(delta_E), np.mean(delta_E), np.max(delta_E))
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Question
I have a .csv of RGB values that I am attempting to convert to Munsell color. The goal is to have a new column added to the .csv that has the munsell values.
My Data
SO-05_Full_Res_Edit.csv
This script works fine for the df within the code. However when I try to expand it to a dataframe based on the attached .csv it keeps giving an error that the Y of xyY needs to be in the domain of [0 to 10]. However even when I check this it still gives the same error.
AssertionError Traceback (most recent call last)
Cell In[47], line 3
1 # Ensure that the Y value is normalized correctly before converting to Munsell
2 df['nY'] = df['nY'].clip(0, 10)
----> 3 df['Munsell'] = df.apply(lambda row: xyY_to_munsell((row['x'], row['y'], row['nY'])), axis=1)
5 # Print the DataFrame with XYZ, xyY, and Munsell columns
6 df.he#1300
#Code
import colour
import numpy as np
import pandas as pd
Example DataFrame of RGB values
data = {
'R': [255, 128, 64, 32, 16],
'G': [0, 255, 128, 64, 32],
'B': [0, 0, 255, 128, 64]
}
df = pd.DataFrame(data)
def rgb_to_xyz(rgb):
# Normalize RGB values to [0, 1]
rgb_normalized = np.array(rgb) / 255.0
# Convert RGB to XYZ
xyz = colour.sRGB_to_XYZ(rgb_normalized)
return xyz
def xyz_to_xyY(xyz):
# Convert XYZ to xyY
xyY = colour.XYZ_to_xyY(xyz)
return xyY
def xyY_to_munsell(xyY):
# Convert xyY to Munsell
munsell_color = colour.xyY_to_munsell_colour(xyY)
return munsell_color
Apply the conversion to each row in the DataFrame
df[['X', 'Y', 'Z']] = df.apply(lambda row: pd.Series(rgb_to_xyz((row['R'], row['G'], row['B']))), axis=1)
df[['x', 'y', 'Y']] = df.apply(lambda row: pd.Series(xyz_to_xyY((row['X'], row['Y'], row['Z']))), axis=1)
df['Munsell'] = df.apply(lambda row: xyY_to_munsell((row['x'], row['y'], row['Y'])), axis=1)
Print the DataFrame with XYZ, xyY, and Munsell columns
df.head()
#print(df)
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