I see a flag. I like flags. Especially the Japanese flags. I don’t specifically care for Japan, but the flag is one of my favourites. I prefer flags with low entropy: so I wrote a script once that ranks the nations flags by entropy so I could quantify my preference. Thanks for letting me infodump a bit.

Edit: Due to people aski g for it: here is the top ten of my ranking:

Nations' flag entropy ranking (n=208). 
Image source: Wikimedia.

0	white_field			-1.439759075204976e-10
1	Indonesia			3.3274441922278752
2	Germany			3.391689777286108
3	South_Ossetia			3.8174437373506778
4	Monaco			3.9718936201427066
5	Poland			3.9719290780440133
6	Austria			4.372592975412404
7	Ukraine			4.405280849871184
8	Hungary			4.4465472496385985
9	Albania			4.6134257669087395
10	Mauritius			4.707109405551959
11	Luxembourg			4.721346585737304

Here’s how I defined the entropy value for each flag:

def color_weighted_spectral_entropy(image):
    b_channel, g_channel, r_channel = cv2.split(image)
    
    # Calculate spectral entropy for each channel
    def channel_spectral_entropy(channel):
        f_transform = np.fft.fft2(channel)
        f_shifted = np.fft.fftshift(f_transform)
        magnitude_spectrum = np.abs(f_shifted)
        if np.sum(magnitude_spectrum) > 0:
            normalized = magnitude_spectrum / np.sum(magnitude_spectrum)
        else:
            normalized = magnitude_spectrum
        # Entropy calculation with color channel weighting
        epsilon = 1e-10
        entropy = -np.sum(normalized * np.log2(normalized + epsilon))
        
        return entropy
    
    weighted_entropy = (
        0.333 * channel_spectral_entropy(b_channel) +
        0.333 * channel_spectral_entropy(g_channel) +
        0.333 * channel_spectral_entropy(r_channel)
    )
    
    return float(weighted_entropy)

“White_field” is just an array that holds zeroes. I use this as a sanity check. Code is on github. I can send DM to whomever is interested. I guess it can also be searched for.