n_clusters = 5 np.random.seed(0) X = face.reshape((-1, 1)) # We need an (n_sample, n_feature) array k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4) k_means.fit(X) values = k_means.cluster_centers_.squeeze() labels = k_means.labels_ # create an array from labels and values face_compressed = np.choose(labels, values) face_compressed.shape = face.shape vmin = face.min() vmax = face.max() # original face plt.figure(1, figsize=(3, 2.2)) plt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256) # compressed face plt.figure(2, figsize=(3, 2.2)) plt.imshow(face_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax) # equal bins face regular_values = np.linspace(0, 256, n_clusters + 1) regular_labels = np.searchsorted(regular_values, face) - 1 regular_values = .5 * (regular_values[1:] + regular_values[:-1]) # mean regular_face = np.choose(regular_labels.ravel(), regular_values, mode="clip") regular_face.shape = face.shape
n_clusters = 5 np.random.seed(0) X = face.reshape((-1, 1)) # We need an (n_sample, n_feature) array k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4) k_means.fit(X) values = k_means.cluster_centers_.squeeze() labels = k_means.labels_ # create an array from labels and values face_compressed = np.choose(labels, values) face_compressed.shape = face.shape vmin = face.min() vmax = face.max() # original face plt.figure(1, figsize=(3, 2.2)) plt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256) # compressed face plt.figure(2, figsize=(3, 2.2)) plt.imshow(face_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax) # equal bins face regular_values = np.linspace(0, 256, n_clusters + 1) regular_labels = np.searchsorted(regular_values, face) - 1 regular_values = .5 * (regular_values[1:] + regular_values[:-1]) # mean regular_face = np.choose(regular_labels.ravel(), regular_values, mode="clip") regular_face.shape = face.shape
np.random.seed(0)
X = face.reshape((-1, 1)) # 拉成一維向量 (n_sample, n_feature) array
k_means = cluster.KMeans(n_clusters=n_clusters,
n_init=4) # K_means分類，num_cluster=5
k_means.fit(X)
values = k_means.cluster_centers_.squeeze(
) # 使用不同 centroid seeds 運行 k-means 算法的時間
labels = k_means.labels_
face_compressed = np.choose(labels,
values) #labels範圍0~4，長度768*1024，values代表五個k_keans值
face_compressed.shape = face.shape
v_min = face.min()
v_max = face.max()
plt.figure(1, figsize=(3, 2.2))
plt.imshow(face, cmap=plt.gray()) #原始影像
plt.figure(1, figsize=(3, 2.2))
plt.imshow(face_compressed.reshape(768, 1024), cmap=plt.gray()) #壓縮後影像
regular_values = np.linspace(0, 256, n_clusters + 1)
# 在regular_values(升冪)插入數組face，返回對應位置
regular_labels = np.searchsorted(regular_values, face) - 1
regular_values = 0.5 * (regular_values[1:] + regular_values[:-1]) #mean
regular_face = np.choose(regular_labels.ravel(), regular_values, mode='clip')
regular_face.shape = face.shape
plt.figure(1, figsize=(3, 2.2))
plt.imshow(regular_face, cmap=plt.gray())
