-
安装：pip install scipy
-
在jupyter中进行聚类示例
import numpy as np
from scipy.cluster.vq import vq, kmeans, whiten
import matplotlib.pyplot as plt
fe = np.array([[1.9,2.0],
[1.7,2.5],
[1.6,3.1],
[0.1,0.1],
[0.8,0.3],
[0.4,0.3],
[0.22,0.1],
[0.4, 0.3],
[0.4,0.5],
[1.8,1.9]])
book = np.array((fe[0], fe[1]))
print(type(book))
print("book: \n",book)
codebook, distortion = kmeans(fe, book)
可以写kmeans(wf,2)， 2表示两个质心，同时启用iter参数
print("codebook:", codebook)
print("distortion: ", distortion)
plt.scatter(fe[:,0], fe[:,1], c='g')
plt.scatter(codebook[:, 0], codebook[:, 1], c='r')
plt.show()
得出结果
- 小图片进行聚类
用PIL生成小尺寸的图片，在小图片上聚类
用resize或者thumbnail（缩略图）
import os
from PIL import Image
import matplotlib.pyplot as plt
os.chdir(r'/Users/liruiying/Documents/pythonclass2021/lesson5')
print(os.getcwd())
im = np.array(Image.open('girl.png'))
#用缩略图聚类
def colorz(filename,n=3):
img=Image.open(filename)
img=img.rotate(-90)
img.thumbnail((200,200))
w,h=img.size
print(w,h)
print('w*h=',w*h)
plt.axis('off')
plt.imshow(img)
plt.show()
points=[]
for count,color in img.getcolors(w*h):
points.append(color)
return points
colorz('girl.png',3)
- 对色彩进行聚类
#对色彩聚类
import numpy as np
from scipy.cluster.vq import vq, kmeans, whiten
import matplotlib.pyplot as plt
points=colorz('girl.png',3)
print(points[0:10])
fe = np.array(points,dtype=float) #聚类需要是Float或者Double
print(fe[0:10])
book =np.array((fe[100],fe[1],fe[8],fe[8])) #聚类中心，初始值
print(type(book))
print("book: \n",book)
#codebook, distortion = kmeans(fe,book)
codebook, distortion = kmeans(fe,7) #7是聚类中心个数
可以写kmeans(wf,2)， 2表示两个质心，同时启用iter参数
print("codebook:", codebook) #聚类中心
centers=np.array(codebook,dtype=int) #变为色彩，还得转为整数
print(centers)
print("distortion: ", distortion)
fe=np.array(points)
plt.scatter(fe[:,0], fe[:,2], c='b')
plt.scatter(codebook[:, 0], codebook[:,2], c='r') #聚类中心
plt.show()
- 导入pycharm
将上列代码合并成ImageColor.py
import numpy as np
from PIL import Image
from scipy.cluster.vq import vq, kmeans, whiten
def colorz(filename,n=3):
img=Image.open(filename)
img=img.rotate(-90)
img.thumbnail((200,200))
w,h=img.size
print(w,h)
print('w*h=',w*h)
points=[]
for count,color in img.getcolors(w*h):
points.append(color)
return points
def kmeansColor(img,n):
points=colorz(img,3)
fe = np.array(points,dtype=float)
codebook, distortion = kmeans(fe,n)
centers=np.array(codebook,dtype=int)
return centers
- 修改main.py
修改一：
from flask import Flask,render_template,request
#增加request
import os
import cv2
import imageColor
#导入imageColor
修改二：
@app.route('/')
def index():
#return "Hi,Flask!"
#genFrame()
picname=request.args.get("picname", type=str)
if not picname:
picname='static/pic/image0.jpg'
pic='static/pic/image'
framecount=825
#imgcolors=imageColor.kmeansColor('static/pic/image0.jpg',5)
imgcolors = imageColor.kmeansColor(picname, 5)
return render_template('index.html',pic1=pic,framecount=framecount,imgcolors=imgcolors)
- 修改index.html
增加以下代码
帧数：{{framecount}}
{{imgcolors}}
{% for c in imgcolors %}
宣传片
{% endfor %}
{% for i in range(framecount) %}
{% endfor %}
- 运行main.py得出结果
点击每个图片，”宣传片”的颜色会根据图片颜色进行变化。
Original: https://blog.csdn.net/m0_61727550/article/details/121278026
Author: Ruiying1020
Title: 【python】Scipy中的聚类算法
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