finalass1.py

import cv2, numpy as np
from sklearn.cluster import KMeans
from matplotlib import pyplot as plt
per=[]
col=[]
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
        per.append("{:0.2f}".format(percent * 100))
        col.append(color/255)
    return rect

# Load image and convert to a list of pixels
image = cv2.imread(r'C:\Users\Dell\Desktop\kr.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)
plt.show()
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_)
plt.imshow(visualize)
plt.show()
# print(per)
# print(col)

lab=['color 1','color 2','color 3','color 4','color 5']
plt.title('Top 5 most prominent colours', fontsize = 24) 
plt.legend(lab, loc = 'upper right') 



explode = (0, 0, 0, 0, 0.1)  # only "explode" the 2nd slice (i.e. 'Hogs')
plt.pie(per,explode=explode,colors=col,labels=lab,shadow=True)
plt.tight_layout() 
plt.show()