How to iterate through non-zeros values of an image ?

I have fond online a function to extract and display the dominant colors of an image. To save time, I want to iterate only on the non-zeros pixels instead of the whole image. However the way I changed the function raises an error :

   if row != [0,0,0]:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Here is the modified code :

def dominantColor(image) :
    from matplotlib.pyplot import imshow, show
    from scipy.cluster.vq import whiten
    from scipy.cluster.vq import kmeans
    import pandas as pd

r = []
g = []
b = []
for row in image :
    if row != [0,0,0]: #the part I added to the original code
        print(row)
        for temp_r, temp_g, temp_b in row:
            r.append(temp_r)
            g.append(temp_g)
            b.append(temp_b)

image_df = pd.DataFrame({'red': r, 'green': g, 'blue': b})
image_df['scaled_color_red'] = whiten(image_df['red'])
image_df['scaled_color_blue'] = whiten(image_df['blue'])
image_df['scaled_color_green'] = whiten(image_df['green'])

cluster_centers, _ = kmeans(image_df[['scaled_color_red','scaled_color_blue','scaled_color_green']], 3)
dominant_colors = []
red_std, green_std, blue_std = image_df[['red','green','blue']].std()
for cluster_center in cluster_centers:
    red_scaled, green_scaled, blue_scaled = cluster_center
    dominant_colors.append((
        red_scaled * red_std / 255,
        green_scaled * green_std / 255,
        blue_scaled * blue_std / 255
    ))
imshow([dominant_colors])
show()

return dominant_colors

How should I correct my iteration loop to remove the error and have only the non-zeros values of my image ? (NB : the image is actually mask * original_image)

CodePudding user response：

You need to add .all() method after that comparison if you want co compare arrays element wise. So if (row == [0,0,0]).all().

import numpy as np

image = np.array([
    [0, 0, 0],
    [1, 0, 0],
    [0, 0, 1],
])

for row in image:
    if not (row == [0, 0, 0]).all():
        print(row)

Result:

[1 0 0]
[0 0 1]

CodePudding user response：

If I understand your code correctly, the answer is in the error log that you posted:

if row != [0,0,0]:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

So, the any function check if any of the elements in the row are evaluated as true:

for row in image :
    if any(row): #enter the if block if any element is not 0
        print(row)
        for temp_r, temp_g, temp_b in row:
            r.append(temp_r)
            g.append(temp_g)
            b.append(temp_b)