[FIXED] How to iterate through non-zeros values of an image ? - Python

Issue

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)

Solution

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]

Answered By - Piotr Grzybowski

Answer Checked By - David Goodson (PHPFixing Volunteer)