# -*- coding: utf-8 -*-
"""
Created on Thu Aug 11 18:54:12 2016 @author: Administrator
""" import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.utils import shuffle
import mahotas as mh original_img = np.array(mh.imread('haonan.jpg'), dtype=np.float64) / 255
original_dimensions = tuple(original_img.shape)
width, height, depth = tuple(original_img.shape)
#(3264L, 2448L, 3L)
image_flattened = np.reshape(original_img, (width * height, depth))
#(7990272L, 3L)
#将原始的图像，变成多行的样式 #打乱图像像素，选取1000个
image_array_sample = shuffle(image_flattened,random_state=0)[:1000] #聚集为64个颜色
estimator = KMeans(n_clusters=64, random_state=0)
estimator.fit(image_array_sample) #Next, we predict the cluster assignment for each of the pixels in the original image:
#将7990272L颜色划分为64种
cluster_assignments = estimator.predict(image_flattened)
'''
cluster_assignments.shape
Out[19]: (7990272L,)
'''
#Finally, we create the compressed image from the compressed palette and cluster assignments: compressed_palette = estimator.cluster_centers_
'''
compressed_palette.shape
Out[3]: (64L, 3L) compressed_palette
Out[4]:
array([[ 0.54188948, 0.66987522, 0.73404635],
[ 0.16122004, 0.20232389, 0.22962963],
[ 0.06970588, 0.06088235, 0.06794118],
[ 0.34392157, 0.46039216, 0.53215686],
[ 0.68235294, 0.29254902, 0.04862745],
[ 0.2619281 , 0.34901961, 0.41911765],
[ 0.68074866, 0.80784314, 0.86737968],
[ 0.54313725, 0.57843137, 0.57647059],
[ 0.47882353, 0.36588235, 0.32117647],
[ 0.11993464, 0.15108932, 0.17821351],
[ 0.7745098 , 0.4745098 , 0.31372549],
[ 0.62459893, 0.73698752, 0.7983066 ],
[ 0.81764706, 0.95098039, 0.57843137],
[ 0.0248366 , 0.01837755, 0.02568243],
[ 0.28912656, 0.22816399, 0.20071301],
[ 0.44456328, 0.44955437, 0.42245989],
[ 0.19869281, 0.27215686, 0.33856209],
[ 0.14588235, 0.12797386, 0.12130719],
[ 0.51568627, 0.21372549, 0.04019608],
[ 0.68333333, 0.59411765, 0.53431373],
[ 0.43227753, 0.5040724 , 0.56440422],
[ 0.37167756, 0.29803922, 0.26143791],
[ 0.73908497, 0.86248366, 0.91477124],
[ 0.55882353, 0.64215686, 0.7004902 ],
[ 0.70812325, 0.72941176, 0.71820728],
[ 0.75215686, 0.37098039, 0.11372549],
[ 0.20980392, 0.72156863, 0.59411765],
[ 0.57896613, 0.69875223, 0.75995247],
[ 0.40588235, 0.08529412, 0.01372549],
[ 0.55764706, 0.45490196, 0.20470588],
[ 0.41921569, 0.56352941, 0.65411765],
[ 0.29877451, 0.4129902 , 0.4877451 ],
[ 0.08686275, 0.12215686, 0.16686275],
[ 0.30532213, 0.32156863, 0.34117647],
[ 0.51980392, 0.61686275, 0.66823529],
[ 0.51078431, 0.51666667, 0.50686275],
[ 0.16642157, 0.24730392, 0.30514706],
[ 0.0629156 , 0.07212276, 0.09445865],
[ 0.6373366 , 0.75955882, 0.82295752],
[ 0.13777778, 0.17934641, 0.20836601],
[ 0.65098039, 0.65588235, 0.66176471],
[ 0.49338235, 0.57867647, 0.63578431],
[ 0.33823529, 0.37205882, 0.37745098],
[ 0.2047619 , 0.30532213, 0.38207283],
[ 0.20980392, 0.04313725, 0.02941176],
[ 0.19758673, 0.2361991 , 0.26033183],
[ 0.59215686, 0.26143791, 0.01699346],
[ 0.24145658, 0.17086835, 0.13893557],
[ 0.50532213, 0.49971989, 0.43417367],
[ 0.79215686, 0.45196078, 0.21372549],
[ 0.12529412, 0.20078431, 0.26431373],
[ 0.59691028, 0.71895425, 0.78193702],
[ 0.51764706, 0.2745098 , 0.17647059],
[ 0.62058824, 0.51911765, 0.46911765],
[ 0.60952381, 0.68095238, 0.73977591],
[ 0.11687812, 0.0946559 , 0.09265667],
[ 0.28627451, 0.25359477, 0.25294118],
[ 0.08411765, 0.09392157, 0.11764706],
[ 0.74845938, 0.76246499, 0.77983193],
[ 0.62287582, 0.26339869, 0.09607843],
[ 0.84313725, 0.94901961, 0.42745098],
[ 0.43267974, 0.41045752, 0.36601307],
[ 0.65918833, 0.77756498, 0.84012768],
[ 0.04037763, 0.03384168, 0.04139434]])
'''
#生成一个新的图像，全部是0，深度和原来图像相等
compressed_img = np.zeros((width, height, compressed_palette.shape[1]))
'''
compressed_palette.shape
Out[7]: (64L, 3L)
'''
label_idx = 0
for i in range(width):
for j in range(height): #首先取出每种颜色的调色索引，然后根据调色索引取颜色值
compressed_img[i][j] = compressed_palette[cluster_assignments[label_idx]]
label_idx += 1 plt.subplot(122)
plt.title('Original Image')
plt.imshow(original_img)
#plt.axis('off')
plt.subplot(121)
plt.title('Compressed Image')
plt.imshow(compressed_img)
#plt.axis('off')
plt.show() '''
在matplotlib下，一个Figure对象可以包含多个子图（Axes），可以使用subplot()快速绘制，
其调用形式如下：subplot(numRows, numCols, plotNum)
图表的整个绘图区域被分成numRows行和numCols列，plotNum参数指定创建的Axes对象所在的区域
如何理解呢？如果numRows ＝ 3，numCols ＝ 2，那整个绘制图表样式为3X2的图片区域，
用坐标表示为（1，1），（1，2），（1，3），（2，1），（2，2），（2，3）。
这时，当plotNum ＝ 1时，表示的坐标为（1，3），即第一行第一列的子图；看代码吧！
'''
import numpy as np
import matplotlib.pyplot as plt
plt.subplot(221) #分成2x2，占用第一个，即第一行第一列的子图
plt.subplot(222)#分成2x2，占用第二个，即第一行第二列的子图
plt.subplot(212)#分成2x1，占用第二个，即第二行
plt.show()