This short python script reads/digitizes an image (JPG/PNG) made with a specific colormap in Python and converts it to another colormap as well as extracts out values.
- Crop the initial image (Figure 1) using other software
Figure 1: Initial uncropped JPG image made in viridis colormap.
- Read the cropped image (Figure 2)
Figure 2: Cropped image made in viridis colormap.
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In the code, mention the input colormap (needs to be in Python).
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Make the map between the input and output colormap.
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Finally, the input image RGB values can be multiplied with the coefficients to get the output image (Figure 3).
Figure 3: Output temperature contour plot with Reds colormap.
hyperbolic-infiltration-theory requires the following packages to function:
- Python version 3.5+
- Numpy >= 1.16
- scipy >=1.5
- matplotlib >=3.3.4
Run the image-digitizer.py Python script.
We make a one-to-one map in between input colormap (RGB) and the Greys (g) colormap. For that we first define the two maps. Then solve for the coefficients a,b,c:
g = a*R + b*G + c*B
For the complete, discretized colormap we can write
b = A * X
with
b = [g_1;g_2;g_3,...], A = [R_1,G_1,B_1; R_2,G_2,B_2; ...] and X = [a;b;c]
So, X = (A^T A)^{-1} A^T b
Once we find the color vector X, we multiply the RGB array in each pixel with it.