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Dominant Colors in an image using python opencv and scikit-learn
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import cv2 | |
from sklearn.cluster import KMeans | |
class DominantColors: | |
CLUSTERS = None | |
IMAGE = None | |
COLORS = None | |
LABELS = None | |
def __init__(self, image, clusters=3): | |
self.CLUSTERS = clusters | |
self.IMAGE = image | |
def dominantColors(self): | |
#read image | |
img = cv2.imread(self.IMAGE) | |
#convert to rgb from bgr | |
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
#reshaping to a list of pixels | |
img = img.reshape((img.shape[0] * img.shape[1], 3)) | |
#save image after operations | |
self.IMAGE = img | |
#using k-means to cluster pixels | |
kmeans = KMeans(n_clusters = self.CLUSTERS) | |
kmeans.fit(img) | |
#the cluster centers are our dominant colors. | |
self.COLORS = kmeans.cluster_centers_ | |
#save labels | |
self.LABELS = kmeans.labels_ | |
#returning after converting to integer from float | |
return self.COLORS.astype(int) | |
img = 'colors.jpg' | |
clusters = 5 | |
dc = DominantColors(img, clusters) | |
colors = dc.dominantColors() | |
print(colors) |
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colors = dc.dominantColors() what does this method do?