How to find the average color of an image in Python with OpenCV?

Question

I tried this code:

import cv2
image = cv2.imread("sample.jpg")
pixel = image[200, 550]
print pixel

But I get an error like:

'Nonetype' no attributes error getitem

This error appears after the third line of code has been executed.

Answer 1

How to fix the error

There are two possible causes for this error:

1. The file name is spelled incorrectly.
2. The image file is not in the current working directory.

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import cv2
import numpy as np
from skimage import io

img = io.imread('/img/8290756f2f65c46a0d638f95cf573a2e.png')[:, :, :-1]

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average = img.mean(axis=0).mean(axis=0)

k- ( n_colors 5).

pixels = np.float32(img.reshape(-1, 3))

n_colors = 5
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
flags = cv2.KMEANS_RANDOM_CENTERS

_, labels, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, flags)
_, counts = np.unique(labels, return_counts=True)

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dominant = palette[np.argmax(counts)]

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In [30]: average
Out[30]: array([91.63179156, 69.30190754, 58.11971896])

In [31]: dominant
Out[31]: array([179.3999  ,  27.341282,   2.294441], dtype=float32)

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:

import matplotlib.pyplot as plt

avg_patch = np.ones(shape=img.shape, dtype=np.uint8)*np.uint8(average)

indices = np.argsort(counts)[::-1]   
freqs = np.cumsum(np.hstack([[0], counts[indices]/counts.sum()]))
rows = np.int_(img.shape[0]*freqs)

dom_patch = np.zeros(shape=img.shape, dtype=np.uint8)
for i in range(len(rows) - 1):
    dom_patch[rows[i]:rows[i + 1], :, :] += np.uint8(palette[indices[i]])

fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12,6))
ax0.imshow(avg_patch)
ax0.set_title('Average color')
ax0.axis('off')
ax1.imshow(dom_patch)
ax1.set_title('Dominant colors')
ax1.axis('off')
plt.show(fig)

TL; DR

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Answer 2

, :

import cv2
import numpy
myimg = cv2.imread('image.jpg')
avg_color_per_row = numpy.average(myimg, axis=0)
avg_color = numpy.average(avg_color_per_row, axis=0)
print(avg_color)

:

[ 197.53434769  217.88439451  209.63799938]

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Answer 3

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Answer 4

Another approach is to use K-Means Clustering to determine the dominant colors in an image usingsklearn.cluster.KMeans()

---

.Input Image

results

C n_clusters=5, the most dominant colors and percent distribution are presented here.

[76.35563647 75.38689122 34.00842057] 7.92%
[200.99049989  31.2085501   77.19445073] 7.94%
[215.62791291 113.68567694 141.34945328] 18.85%
[223.31013152 172.76629675 188.26878339] 29.26%
[234.03101989 217.20047979 229.2345317 ] 36.03%

Visualization of each color cluster

Similarity to n_clusters=10,

[161.94723762 137.44656853 116.16306634] 3.13%
[183.0756441    9.40398442  50.99925105] 4.01%
[193.50888866 168.40201684 160.42104169] 5.78%
[216.75372674  60.50807092 107.10928817] 6.82%
[73.18055782 75.55977818 32.16962975] 7.36%
[226.25900564 108.79652434 147.49787087] 10.44%
[207.83209569 199.96071651 199.48047163] 10.61%
[236.01218943 151.70521203 182.89174295] 12.86%
[240.20499237 189.87659523 213.13580544] 14.99%
[235.54419627 225.01404087 235.29930545] 24.01%

import cv2, numpy as np
from sklearn.cluster import KMeans

def visualize_colors(cluster, centroids):
    # Get the number of different clusters, create histogram, and normalize
    labels = np.arange(0, len(np.unique(cluster.labels_)) + 1)
    (hist, _) = np.histogram(cluster.labels_, bins = labels)
    hist = hist.astype("float")
    hist /= hist.sum()

    # Create frequency rect and iterate through each cluster color and percentage
    rect = np.zeros((50, 300, 3), dtype=np.uint8)
    colors = sorted([(percent, color) for (percent, color) in zip(hist, centroids)])
    start = 0
    for (percent, color) in colors:
        print(color, "{:0.2f}%".format(percent * 100))
        end = start + (percent * 300)
        cv2.rectangle(rect, (int(start), 0), (int(end), 50), \
                      color.astype("uint8").tolist(), -1)
        start = end
    return rect

# Load image and convert to a list of pixels
image = cv2.imread('1.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
reshape = image.reshape((image.shape[0] * image.shape[1], 3))

# Find and display most dominant colors
cluster = KMeans(n_clusters=5).fit(reshape)
visualize = visualize_colors(cluster, cluster.cluster_centers_)
visualize = cv2.cvtColor(visualize, cv2.COLOR_RGB2BGR)
cv2.imshow('visualize', visualize)
cv2.waitKey()