XML anchors clustering of VOC data format calculated by Python + kmeans

-Pastoral- 2020-11-13 08:02:35
xml anchors clustering voc data


#!/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'
#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
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]
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):
def _write_to_txt(self):
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)
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)
def _load_data (self):
self.data = np.loadtxt(self.path)
def parse_data (self):
self.y_k = self.km.fit_predict(self.data)
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")
@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") # Designated as voc Label path , And the path of storing the generated files
kmean_parse = kMean_parse(n_clusters,"./data1.txt")
kmean_parse.plot_data() # Icon
if __name__ == '__main__':


  • “xml_path” Designated for marking xml File path ;

  • “width_hight” Specify the training image size ;

  • “n_clusters” Specify the number of clusters ;


Output after running n_clusters individual 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 ]]

Icon :



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