Python数据分析与挖掘实战（第2版）张良均
第9章 基于水色图像的水质评价
参考源码内容，对运行报错部分进行更改
import numpy as np
import os,re
from PIL import Image
# 图像切割及特征提取
path = 'images/' # 图片所在路径
# 自定义获取图片名称函数
def getImgNames(path=path):
'''
获取指定路径中所有图片的名称
:param path: 指定的路径
:return: 名称列表
'''
filenames = os.listdir(path)
imgNames = []
for i in filenames:
if re.findall('^\d_\d+\.jpg$', i) != []:
imgNames.append(i)
return imgNames
# 自定义获取三阶颜色矩函数
def Var(data=None):
'''
获取给定像素值矩阵的三阶颜色矩
:param data: 给定的像素值矩阵
:return: 对应的三阶颜色矩
'''
x = np.mean((data-data.mean())**3)
return np.sign(x)*abs(x)**(1/3)
# 批量处理图片数据
imgNames = getImgNames(path=path) # 获取所有图片名称
n = len(imgNames) # 图片张数
data = np.zeros([n, 9]) # 用来装样本自变量
labels = np.zeros([n]) # 用来放样本标签
for i in range(n):
img = Image.open(path+imgNames[i]) # 读取图片
M,N = img.size # 图片像素的尺寸
img = img.crop((M/2-50,N/2-50,M/2+50,N/2+50)) # 图片切割
r,g,b = img.split() # 将图片分割成三通道
rd = np.asarray(r)/255 # 转化成数组数据
gd = np.asarray(g)/255
bd = np.asarray(b)/255
data[i,0] = rd.mean() # 一阶颜色矩
data[i,1] = gd.mean()
data[i,2] = bd.mean()
data[i,3] = rd.std() # 二阶颜色矩
data[i,4] = gd.std()
data[i,5] = bd.std()
data[i,6] = Var(rd) # 三阶颜色矩
data[i,7] = Var(gd)
data[i,8] = Var(bd)
labels[i] = imgNames[i][0] # 样本标签
from sklearn.model_selection import train_test_split
# 数据拆分,训练集、测试集
data_tr,data_te,label_tr,label_te = train_test_split(data,labels,test_size=0.3,
random_state=10)
from sklearn.tree import DecisionTreeClassifier
# 模型训练
model = DecisionTreeClassifier(random_state=5).fit(data_tr, label_tr)
# 水质评价
from sklearn.metrics import confusion_matrix
pre_te = model.predict(data_te)
# 混淆矩阵
cm_te = confusion_matrix(label_te,pre_te)
print(cm_te)
from sklearn.metrics import accuracy_score
# 准确率
print(accuracy_score(label_te,pre_te))
[[14 0 2 0 0]
[ 1 14 0 0 0]
[ 1 0 21 0 0]
[ 0 1 1 5 0]
[ 0 1 0 0 0]]
0.8852459016393442
