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83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
# import matplotlib.pyplot as plt
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import numpy as np
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import scipy.spatial
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from skimage import io
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import time
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def distance(points,Pc):
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return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])
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def kmeans(points = [0,0], K = 1):
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# Initialisation K prototypes
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dim = points.shape[1]
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N = points.shape[0]
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iter = 0
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eps = 0.1
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Pc_index = []
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Pc_save = np.zeros([K,dim])
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clusters = []
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for i in range(0,K):
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Pc_index.append(np.random.randint(0,N))
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Pc = points[Pc_index,:]
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while (np.mean(distance(Pc,Pc_save)) > eps and iter < 3):
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iter += 1
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Pc_save = Pc
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# print(Pc)
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# print(points[:,:Pc.shape[0]])
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dist = distance(points=points[:,:Pc.shape[1]],Pc=Pc)
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clust = np.argmin(dist, axis=1)
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clust = np.expand_dims(clust, axis=0)
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points = np.append(points[:,:Pc.shape[1]], clust.T, axis=1)
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# print(points)
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Pc = np.zeros([K,dim])
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index = np.array([])
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for n in range(0,N):
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for k in range(0,K):
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index = np.append(index, (clust==k).sum())
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if points[n,-1] == k:
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# print(points)
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# print(Pc)
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Pc[k,:] = np.add(Pc[k,:], points[n,:-1])
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for k in range(0,K):
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Pc[k,:] = np.divide(Pc[k,:],index[k])
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# print(Pc)
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indice = points[:,-1]
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points = points[:,:-1]
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return Pc, indice, points
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def mat_2_img(mat,my_img):
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img_seg = mat.reshape(my_img.shape[0], my_img.shape[1], my_img.shape[2])
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return img_seg
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def img_2_mat(my_img):
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mat = my_img.reshape(my_img.shape[0]*my_img.shape[1],my_img.shape[2])
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return mat
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def kmeans_image(path_image, K):
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my_img = io.imread(path_image)
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# imgplot = plt.imshow(my_img)
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Mat = img_2_mat(my_img)
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Pc, index, clusters = kmeans(Mat, K)
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for k in range(Mat.shape[0]):
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Mat[k,:] = np.floor(Pc[index[k],:])
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img_seg = mat_2_img(Mat, my_img)
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io.imsave(path_image.split('.')[0] + "_%d.jpg" % K, img_seg)
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# imgplot = plt.imshow(img_seg)
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return Pc, index, img_seg
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path_image = "fruits.jpg"
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start_time = time.time()
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Pc, index, img_seg = kmeans_image(path_image=path_image, K=2)
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end_time = time.time()
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print(f"It took {end_time-start_time:.2f} seconds to compute")
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