python+kmeans计算VOC数据格式xml anchors聚类

-牧野- 2020-11-13 08:02:35
Python 计算 python+kmeans kmeans voc


 

#!/usr/bin/env python
# -*- coding: utf8 -*-
import sys
from xml.etree import ElementTree
from lxml import etree
import numpy as np
import os
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import click
XML_EXT = '.xml'
ENCODE_METHOD = 'utf-8'
#pascalVocReader readers the voc xml files parse it
class PascalVocReader:
"""
this class will be used to get transfered width and height from voc xml files
"""
def __init__(self, filepath,width,height):
# shapes type:
# [labbel, [(x1,y1), (x2,y2), (x3,y3), (x4,y4)], color, color, difficult]
self.shapes = []
self.filepath = filepath
self.verified = False
self.width=width
self.height=height
try:
self.parseXML()
except:
pass
def getShapes(self):
return self.shapes
def addShape(self, bndbox, width,height):
xmin = int(bndbox.find('xmin').text)
ymin = int(bndbox.find('ymin').text)
xmax = int(bndbox.find('xmax').text)
ymax = int(bndbox.find('ymax').text)
width_trans = (xmax - xmin)/width*self.width
height_trans = (ymax-ymin)/height *self.height
points = [width_trans,height_trans]
self.shapes.append((points))
def parseXML(self):
assert self.filepath.endswith(XML_EXT), "Unsupport file format"
parser = etree.XMLParser(encoding=ENCODE_METHOD)
xmltree = ElementTree.parse(self.filepath, parser=parser).getroot()
pic_size = xmltree.find('size')
size = (int(pic_size.find('width').text),int(pic_size.find('height').text))
for object_iter in xmltree.findall('object'):
bndbox = object_iter.find("bndbox")
self.addShape(bndbox, *size)
return True
class create_w_h_txt:
def __init__(self,vocxml_path,width_hight,txt_path):
self.voc_path = vocxml_path
self.txt_path = txt_path
self.width_hight = width_hight
def _gether_w_h(self):
pass
def _write_to_txt(self):
pass
def process_file(self):
file_w = open(self.txt_path,'w')
# print (self.txt_path)
for file in os.listdir(self.voc_path):
file_path = os.path.join(self.voc_path, file)
xml_parse = PascalVocReader(file_path,self.width_hight[0],self.width_hight[1])
data = xml_parse.getShapes()
for w,h in data :
txtstr = str(w)+' '+str(h)+'\n'
#print (txtstr)
file_w.write(txtstr)
file_w.close()
class kMean_parse:
def __init__(self,n_clusters,path_txt):
self.n_clusters = n_clusters
self.path = path_txt
self.km = KMeans(n_clusters=self.n_clusters,init="k-means++",n_init=10,max_iter=3000000,tol=1e-3,random_state=0)
self._load_data()
def _load_data (self):
self.data = np.loadtxt(self.path)
def parse_data (self):
self.y_k = self.km.fit_predict(self.data)
print(self.km.cluster_centers_)
def plot_data (self):
cValue = ['orange','r','y','green','b','gray','black','purple','brown','tan']
for i in range(self.n_clusters):
plt.scatter(self.data[self.y_k == i, 0], self.data[self.y_k == i, 1], s=50, c=cValue[i%len(cValue)], marker="o",
label="cluster "+str(i))
# draw the centers
plt.scatter(self.km.cluster_centers_[:, 0], self.km.cluster_centers_[:, 1], s=250, marker="*", c="red", label="cluster center")
plt.legend()
plt.grid()
plt.show()
@click.command()
@click.option('--xml_path', default='/media/sdb/datasets/label', help='path of xml label')
@click.option('--width_hight', default=[416,416], help='width and hight of training input')
@click.option('--n_clusters', default=9, help='number of clusters')
def get_anchors(xml_path,width_hight,n_clusters):
whtxt = create_w_h_txt(xml_path,width_hight,"./data1.txt") #指定为voc标注路径,以及存放生成文件路径
whtxt.process_file()
kmean_parse = kMean_parse(n_clusters,"./data1.txt")
kmean_parse.parse_data()
kmean_parse.plot_data() # 图示
if __name__ == '__main__':
get_anchors()

 

  • “xml_path” 指定打标的xml文件所在路径;

  • “width_hight”指定训练时图像大小;

  • “n_clusters”指定聚类种类数;

 

运行后输出的 n_clusters 个 anchor:

[[198.96711509 188.58921169]
 [ 67.11470053  70.1287722 ]
 [283.15663365 282.96021749]
 [ 85.24650053 162.72464146]
 [373.29416408 359.19896709]
 [259.06200681 369.32829768]
 [368.76172079 206.79669921]
 [165.36211638 339.71367893]
 [106.91206844 259.0938661 ]]

图示:

 

版权声明
本文为[-牧野-]所创,转载请带上原文链接,感谢
https://blog.csdn.net/dcrmg/article/details/93868585

  1. 利用Python爬虫获取招聘网站职位信息
  2. Using Python crawler to obtain job information of recruitment website
  3. Several highly rated Python libraries arrow, jsonpath, psutil and tenacity are recommended
  4. Python装饰器
  5. Python实现LDAP认证
  6. Python decorator
  7. Implementing LDAP authentication with Python
  8. Vscode configures Python development environment!
  9. In Python, how dare you say you can't log module? ️
  10. 我收藏的有关Python的电子书和资料
  11. python 中 lambda的一些tips
  12. python中字典的一些tips
  13. python 用生成器生成斐波那契数列
  14. python脚本转pyc踩了个坑。。。
  15. My collection of e-books and materials about Python
  16. Some tips of lambda in Python
  17. Some tips of dictionary in Python
  18. Using Python generator to generate Fibonacci sequence
  19. The conversion of Python script to PyC stepped on a pit...
  20. Python游戏开发,pygame模块,Python实现扫雷小游戏
  21. Python game development, pyGame module, python implementation of minesweeping games
  22. Python实用工具,email模块,Python实现邮件远程控制自己电脑
  23. Python utility, email module, python realizes mail remote control of its own computer
  24. 毫无头绪的自学Python,你可能连门槛都摸不到!【最佳学习路线】
  25. Python读取二进制文件代码方法解析
  26. Python字典的实现原理
  27. Without a clue, you may not even touch the threshold【 Best learning route]
  28. Parsing method of Python reading binary file code
  29. Implementation principle of Python dictionary
  30. You must know the function of pandas to parse JSON data - JSON_ normalize()
  31. Python实用案例,私人定制,Python自动化生成爱豆专属2021日历
  32. Python practical case, private customization, python automatic generation of Adu exclusive 2021 calendar
  33. 《Python实例》震惊了,用Python这么简单实现了聊天系统的脏话,广告检测
  34. "Python instance" was shocked and realized the dirty words and advertisement detection of the chat system in Python
  35. Convolutional neural network processing sequence for Python deep learning
  36. Python data structure and algorithm (1) -- enum type enum
  37. 超全大厂算法岗百问百答(推荐系统/机器学习/深度学习/C++/Spark/python)
  38. 【Python进阶】你真的明白NumPy中的ndarray吗?
  39. All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)
  40. [advanced Python] do you really understand ndarray in numpy?
  41. 【Python进阶】Python进阶专栏栏主自述:不忘初心,砥砺前行
  42. [advanced Python] Python advanced column main readme: never forget the original intention and forge ahead
  43. python垃圾回收和缓存管理
  44. java调用Python程序
  45. java调用Python程序
  46. Python常用函数有哪些?Python基础入门课程
  47. Python garbage collection and cache management
  48. Java calling Python program
  49. Java calling Python program
  50. What functions are commonly used in Python? Introduction to Python Basics
  51. Python basic knowledge
  52. Anaconda5.2 安装 Python 库(MySQLdb)的方法
  53. Python实现对脑电数据情绪分析
  54. Anaconda 5.2 method of installing Python Library (mysqldb)
  55. Python implements emotion analysis of EEG data
  56. Master some advanced usage of Python in 30 seconds, which makes others envy it
  57. python爬取百度图片并对图片做一系列处理
  58. Python crawls Baidu pictures and does a series of processing on them
  59. python链接mysql数据库
  60. Python link MySQL database