import numpy as np import cv2 import matplotlib.pyplot as plt image=cv2.imread('../input/hillstation/hillstation.jpg') plt.imshow(image) #original image 0x7f8efaabf890>
i=image.sum(axis=2) #convert the shape of image in 2 dimensions i.shape (183, 275) img=cv2.cvtColor(image,cv2.COLOR_BGR2RGB) plt.imshow(img) #convert the image into RGB 0x7f8efa9e9550>
vector=img.reshape((-1,3)) #convert the mXNX3 image into kX3 matrix where k=mXn and each row will be a vector in 3 dimensions space vector array([[ 55, 61, 111], [ 55, 61, 111], [ 55, 61, 113], ..., [ 42, 40, 25], [ 35, 33, 18], [ 28, 26, 13]], dtype=uint8) vector=np.float32(vector) #convert the uint8 values to float values. k-means method to opencv vector array([[ 55., 61., 111.], [ 55., 61., 111.], [ 55., 61., 113.], ..., [ 42., 40., 25.], [ 35., 33., 18.], [ 28., 26., 13.]], dtype=float32)
#clustering into multiple labels as the picture has multiple colours. c=(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10,1.0)
# first parameter is used for stop the criteria if the accuracy is achieved # second parameter is used for stop the algorithm after he specified number of iterations
# 'c' is the iteration termination process. When the iteration is satisfied, the algorithm will stop. k=5#number of clusters attempts=10#number of times the algorithm is executed using different labelings. ret,label,center=cv2.kmeans(vector,k,None,c,attempts, cv2.KMEANS_PP_CENTERS)
#cv2.kmeans_pp_centers is used to specify how initial centers are taken center=np.uint8(center) res=center[label.flatten()] #access the label to regenerate the image im=res.reshape(img.shape)
