Find dominant color on an image

Alternatively you could try a k-means approach. Calculate k clusters with k ~ 2..5 and take the centroid of the biggest group as your dominant color.

The python docu of OpenCv has an illustrated example that gets the dominant color(s) pretty well:

Here's a Python approach using K-Means Clustering to determine the dominant colors in an image with sklearn.cluster.KMeans()

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Input image

Results

With n_clusters=5, here are the most dominant colors and percentage distribution

[14.69488554 34.23074345 41.48107857] 13.67%
[141.44980073 207.52576948 236.30722987] 15.69%
[ 31.75790423  77.52713644 114.33328324] 18.77%
[ 48.41205713 118.34814452 176.43411287] 25.19%
[ 84.04820266 161.6848298  217.14045211] 26.69%

Visualization of each color cluster

Similarity with n_clusters=10,

[ 55.09073171 113.28271003  74.97528455] 3.25%
[ 85.36889668 145.80759374 174.59846237] 5.24%
[164.17201088 223.34258123 241.81929254] 6.60%
[ 9.97315932 22.79468111 22.01822211] 7.16%
[19.96940211 47.8375841  72.83728002] 9.27%
[ 26.73510467  70.5847759  124.79314278] 10.52%
[118.44741779 190.98204701 230.66728334] 13.55%
[ 51.61750364 130.59930047 198.76335878] 13.82%
[ 41.10232129 104.89923271 160.54431333] 14.53%
[ 81.70930412 161.823664   221.10258949] 16.04%

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's 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.jpg')
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()

The solution

• Find H-S histogram
• Find peak H value(using minmaxLoc function)
• Split image 3 channel(h,s,v)
• Apply to threshold.
• Create image by merge 3 channel