ImageCompress
K-means聚类算法实现图片的压缩，Python实现
参数
- K 全图用多少种颜色表示，默认值 K=16，设置K后将会对所有图像进行聚类，并只使用K种颜色表示全图
- iter 迭代计算次数, 默认值 iter=3
未实现
- 未实现压缩后的存储（降低图像文件大小），只能存为一般jpg格式预览压缩后效果
效果
默认参数下的效果
核心代码：
import numpy as np
from PIL import Image
import random
class KMCompress():
def __init__(self):
self.centroids = None #簇中心 (K, c)
self.data = None #压缩后的数据 (h*w, c)
self.cen_idx = None #压缩后的数据对应簇中心的下标(h*w, 1)
self.shape = None #原图的shape
def compress(self, path, K=16, iter=3):
'''
压缩图像
:param path: 图像路径
:param K: K簇，压缩后图像使用K个像素点表示全图
:param iter: 迭代计算次数
:return: None
'''
X, self.shape = self.__image_to_matrix(path)
centroids = self.__init_random_centroids(X, K)
for i in range(iter):
print(' ====> ' + str((i+1.0)*100 / (iter + 1)) + '%')
cen_idx = self.__find_closest_centroids(X, centroids)
centroids = self.__move_centroids(X, cen_idx, K)
print(' ====> 99%')
cen_idx = self.__find_closest_centroids(X, centroids)
self.cen_idx = cen_idx
self.centroids = centroids
self.__fill_data()
def __fill_data(self):
m = self.cen_idx.shape[0]
channel = self.shape[2]
self.data = np.zeros((m, channel))
for i in range(m):
self.data[i] = self.centroids[int(self.cen_idx[i])]
def __image_to_matrix(self, path):
'''
加载图片
:param path:
:return: 图像矩阵 (h * w, c)
'''
img = Image.open(path)
w, h = img.size
if img.mode == 'RGB':
shape = (h, w, 3)
elif img.mode == 'L':
shape = (h, w, 1)
return np.asmatrix(img.getdata(), dtype='float'), shape
def __init_random_centroids(self, X, K):
'''
初始化簇中心
:param X: 图像矩阵 (h*w, c)
:param K:
:return: 簇中心(K, c)
'''
m, n = X.shape
sh = list(range(m))
random.shuffle(sh)
centroids = []
for i in range(K):
centroids.append(X[sh[i]])
return (np.asarray(centroids))[:,0,:] #list 转换成Matrix
def __find_closest_centroids(self, img, centroids):
'''
计算图像每个像素点对应的簇中心下标
:param img:
:param centroids: (h*w, 1)
:return: 图像每个像素点对应的簇中心下标
'''
m, n = img.shape
K, _ = centroids.shape
cen_idx = np.zeros([m, 1], dtype='int')
for i in range(m):
M = np.full([K, n], img[i])
err = M - centroids
distance = np.multiply(err, err).sum(axis=1)
cen_idx[i] = distance.argmin() #最小列索引
return cen_idx
def __move_centroids(self, img, cen_idx, K):
'''
更新簇中心
:param img:
:param cen_idx: 图像每个像素点对应的簇中心下标
:param K: K簇
:return: 簇中心矩阵 (K, c)
'''
m, n = img.shape
times = np.zeros([K, 1], dtype='int')
centroids = np.zeros([K, n], dtype='float')
for i in range(m):
idx = int(cen_idx[i])
centroids[idx] = centroids[idx] + img[i]
times[idx] = times[idx] + 1
while times.min() == 0: #避免除零异常
times[times.argmin()] = 1
centroids = centroids / times
return centroids
def get_img(self):
'''
返回图像文件
:param M:
:param shape:
:return:
'''
img = np.reshape(np.asarray(self.data), self.shape)
return Image.fromarray(img.astype(np.uint8))
def save(self, path):
'''
#TODO 字典+二进制压缩存储图片
:param path:
:return:
'''
pass
def load(self, path):
'''
TODO 读取压缩后的图片
:param path:
:return:
'''
pass
a = KMCompress()
a.compress('./b.jpg', iter=10)
a.get_img().save('./compress_b.jpg')
print('done...')
代码地址：https://github.com/SherlockUnknowEn/ImageCompress.git
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