问题描述：把给定图片，用图片中最主要的三种颜色来表示该图片
k-means思想：
1、选择k个点作为初始中心
2、将每个点指派到最近的中心，形成k个簇cluster
3、重新计算每个簇的中心
4、如果簇中心发生明显变化或未达到最大迭代次数，则回到step2
问题：初始点不对的时候，容易收敛到局部最优值
解决办法：
1、选择k个点作为初始中心——canopy，模拟退火，贝叶斯准则
2、将每个点指派到最近的中心，形成k个簇cluster
3、重新计算每个簇的中心
4、如果簇中心发生了明显的变化或未达到最大迭代次数，则回到step2
例子：给你一幅图像，找出其中最主要的三种颜色，并将图片用三种最主要的颜色表示出来
# -*- coding: utf-8 -*-
# https://github.com/ZeevG/python-dominant-image-colour
# commented by heibanke from PIL import Image
import random
import numpy class Cluster(object):
"""
pixels: 主要颜色所依据的像素点
centroid: 主要颜色的RGB值
"""
def __init__(self):
self.pixels = []
self.centroid = None
#cluster有两个属性，centroid表示聚类中心，pixels表示依附于该聚类中心的那些像素点
#每个聚类中心都是一个单独的Cluster对象
def addPoint(self, pixel):
self.pixels.append(pixel) def setNewCentroid(self):
"""
通过pixels均值重新计算主要颜色
"""
R = [colour[0] for colour in self.pixels]
G = [colour[1] for colour in self.pixels]
B = [colour[2] for colour in self.pixels] R = sum(R) / len(R)
G = sum(G) / len(G)
B = sum(B) / len(B) self.centroid = (R, G, B)
self.pixels = [] return self.centroid class Kmeans(object): def __init__(self, k=3, max_iterations=5, min_distance=5.0, size=400):
"""
k: 主要颜色的分类个数
max_iterations: 最大迭代次数
min_distance: 当新的颜色和老颜色的距离小于该最小距离时，提前终止迭代
size: 用于计算的图像大小
"""
self.k = k
self.max_iterations = max_iterations
self.min_distance = min_distance
self.size = (size, size) def run(self, image):
self.image = image
#生成缩略图，节省运算量
self.image.thumbnail(self.size)
self.pixels = numpy.array(image.getdata(), dtype=numpy.uint8)
self.clusters = [None]*self.k
self.oldClusters = None
#在图像中随机选择k个像素作为初始主要颜色
randomPixels = random.sample(self.pixels, self.k) for idx in range(self.k):
self.clusters[idx] = Cluster()#生成idx个Cluster的对象
self.clusters[idx].centroid = randomPixels[idx]#每个centroid是随机采样得到的 iterations = 0 #开始迭代
while self.shouldExit(iterations) is False:
self.oldClusters= [cluster.centroid for cluster in self.clusters]
print iterations #对pixel和self.clusters中的主要颜色分别计算距离，将pixel加入到离它最近的主要颜色所在的cluster中
for pixel in self.pixels:
self.assignClusters(pixel)
#对每个cluster中的pixels，重新计算新的主要颜色
for cluster in self.clusters:
cluster.setNewCentroid() iterations += 1 return [cluster.centroid for cluster in self.clusters] def assignClusters(self, pixel):
shortest = float('Inf')
for cluster in self.clusters:
distance = self.calcDistance(cluster.centroid, pixel)
if distance < shortest:
shortest = distance
nearest = cluster#nearest实际上是cluster的引用，不是复制
nearest.addPoint(pixel) def calcDistance(self, a, b):
result = numpy.sqrt(sum((a - b) ** 2))
return result def shouldExit(self, iterations): if self.oldClusters is None:
return False
#计算新的中心和老的中心之间的距离
for idx in range(self.k):
dist = self.calcDistance(
numpy.array(self.clusters[idx].centroid),
numpy.array(self.oldClusters[idx])
)
if dist < self.min_distance:
return True if iterations <= self.max_iterations:
return False return True # The remaining methods are used for debugging
def showImage(self):
"""
显示原始图像
"""
self.image.show() def showCentroidColours(self):
"""
显示主要颜色
"""
for cluster in self.clusters:
image = Image.new("RGB", (200, 200), cluster.centroid)
image.show() def showClustering(self):
"""
将原始图像的像素完全替换为主要颜色后的效果
"""
localPixels = [None] * len(self.image.getdata()) #enumerate用于既需要遍历元素下边也需要得到元素值的情况，用for循环比较麻烦
for idx, pixel in enumerate(self.pixels):
shortest = float('Inf') #正无穷
for cluster in self.clusters:
distance = self.calcDistance(
cluster.centroid,
pixel
)
if distance < shortest:
shortest = distance
nearest = cluster localPixels[idx] = nearest.centroid w, h = self.image.size
localPixels = numpy.asarray(localPixels)\
.astype('uint8')\
.reshape((h, w, 3)) colourMap = Image.fromarray(localPixels)
return colourMap if __name__=="__main__":
from PIL import Image
import os k_image=Kmeans(k=3) #默认参数
path = './pics/'
fp = open('file_color.txt','w')
for filename in os.listdir(path):
print path+filename
try:
color = k_image.run(Image.open(path+filename))
# w_image = k_image.showClustering()
w_image = k_image.showCentroidColours()
w_image.save(path+'mean_'+filename,'jpeg')
fp.write('The color of '+filename+' is '+str(color)+'\n')
except:
print "This file format is not support"
fp.close()
处理前的图片：
处理后的图片：
参考：http://blog.zeevgilovitz.com/detecting-dominant-colours-in-python/