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count_head_area.py
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#
# #读取图片和xml文件并将其信息写入到txt中
# import os
# import random
# import xml.etree.ElementTree as ET
#
# from utils.config import image_path,xml_path,all_dataset,train_dataset,test_dataset
#
# #读取xml文件得到所有标注框
# def convert_annotation(image_id, list_file):
# # rb:二进制文件
# in_file = open(os.path.join(xml_path,'%s.xml'%(image_id)),"rb")
# tree=ET.parse(in_file)
# root = tree.getroot()
#
# for obj in root.iter('object'):
#
# #读取难识别标签
# read_difficult=False
# if read_difficult:
# if obj.find('difficult') != None:
# difficult = obj.find('difficult').text
# if int(difficult) == 1:
# continue
#
# xmlbox = obj.find('bndbox')
# b = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)), int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text)))
# list_file.write(" " + ",".join([str(a) for a in b]) )
# list_file.write('\n')
#
# if __name__ == '__main__':
# #读取所有数据集
# list_file = open(all_dataset, 'w')
# for file in os.listdir(image_path):
# image_id = os.path.splitext(file)[0] #获取文件名
# list_file.write(os.path.join(image_path, file)) #写图片完整路径
# convert_annotation(image_id, list_file) #写标注框
# list_file.close()
#
# # -*- coding:utf-8 -*-
# import os
#
# import xml.dom.minidom
#
# xml_file_path = "/home/lyz/data/VOCdevkit/MyDataSet/Annotations/"
# lst_label = ["height", "width", "depth"]
# lst_dir = os.listdir(xml_file_path)
#
#
# for file_name in lst_dir:
# file_path = xml_file_path + file_name
# tree = xml.dom.minidom.parse(file_path)
# root = tree.documentElement #获取根结点
# size_node = root.getElementsByTagName("size")[0]
# for size_label in lst_label: #替换size标签下的子节点
# child_tag = "img_" + size_label
# child_node = size_node.getElementsByTagName(child_tag)[0]
# new_node = tree.createElement(size_label)
# text = tree.createTextNode(child_node.firstChild.data)
# new_node.appendChild(text)
# size_node.replaceChild(new_node, child_node)
#
# #替换object下的boundingbox节点
# lst_obj = root.getElementsByTagName("object")
# data = {}
# for obj_node in lst_obj:
# box_node = obj_node.getElementsByTagName("bounding_box")[0]
# new_box_node = tree.createElement("bndbox")
# for child_node in box_node.childNodes:
# tmp_node = child_node.cloneNode("deep")
# new_box_node.appendChild(tmp_node)
# x_node = new_box_node.getElementsByTagName("x_left_top")[0]
# xmin = x_node.firstChild.data
# data["xmin"] = (xmin, x_node)
# y_node = new_box_node.getElementsByTagName("y_left_top")[0]
# ymin = y_node.firstChild.data
# data["ymin"] = (ymin, y_node)
# w_node = new_box_node.getElementsByTagName("width")[0]
# xmax = str(int(xmin) + int(w_node.firstChild.data))
# data["xmax"] = (xmax, w_node)
# h_node = new_box_node.getElementsByTagName("height")[0]
# ymax = str(int(ymin) + int(h_node.firstChild.data))
# data["ymax"] = (ymax, h_node)
#
#
# for k, v in data.items():
# new_node = tree.createElement(k)
# text = tree.createTextNode(v[0])
# new_node.appendChild(text)
# new_box_node.replaceChild(new_node, v[1])
# obj_node.replaceChild(new_box_node, box_node)
#
# with open(file_path, 'w') as f:
# tree.writexml(f, indent="\n", addindent="\t", encoding='utf-8')
#
# #去掉XML文件头（一些情况下文件头的存在可能导致错误）
# lines = []
# with open(file_path, 'rb') as f:
# lines = f.readlines()[1:]
# with open(file_path, 'wb') as f:
# f.writelines(lines)
#
# print("-----------------done--------------------")
import xml.dom.minidom
import os
import random
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# 这两行代码解决 plt 中文显示的问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
count_headnum = [0 for i in range(3070)]
count_headarea = [0 for i in range(106304)]
count_headnum = [0 for i in range(3070)]
x_min = [0 for i in range(106304)]
y_min = [0 for i in range(106304)]
x_max = [0 for i in range(106304)]
y_max = [0 for i in range(106304)]
i = 0
sum = 0
j = 0
#存放xml文件的地址
xml_file_path = r"D:/python_files/classroom/Annotations/"
lst_dir = os.listdir(xml_file_path)
for file_name in lst_dir:
#读入所有的xml文件
file_path = xml_file_path + file_name
tree = xml.dom.minidom.parse(file_path)
#获取根节点
root = tree.documentElement
persons = root.getElementsByTagName("object")
for person in persons:
count_headnum[i] = count_headnum[i] + 1
head_area = person.getElementsByTagName("bndbox")[0]
x_min[j] = head_area.getElementsByTagName("xmin")[0].firstChild.data
x_max[j] = head_area.getElementsByTagName("xmax")[0].firstChild.data
y_min[j] = head_area.getElementsByTagName("ymin")[0].firstChild.data
y_max[j] = head_area.getElementsByTagName("ymax")[0].firstChild.data
count_headarea[j] = (int(x_max[j]) - int(x_min[j]))*(int(y_max[j]) - int(y_min[j]))
j = j + 1
i = i + 1
# print(count_headnum.shape)
# random.shuffle(count_headnum) # 打乱列表顺序
#
# train = count_headnum[:1799]
# val = count_headnum[1800:2099]
# test = count_headnum[2100:]
# def count_(train,count_train):
# count_train = [0 for i in range(11)]
# for i in range(len(train)):
# if train[i]>=0 and train[i]<10 :
# count_train[0] = count_train[0] + 1
# if train[i] >= 10 and train[i] < 20:
# count_train[1] = count_train[1] + 1
# if train[i] >= 20 and train[i] < 30:
# count_train[2] = count_train[2] + 1
# if train[i] >= 30 and train[i] < 40:
# count_train[3] = count_train[3] + 1
# if train[i] >= 40 and train[i] < 50:
# count_train[4] = count_train[4] + 1
# if train[i] >= 50 and train[i] < 60:
# count_train[5] = count_train[5] + 1
# if train[i] >= 60 and train[i] < 70:
# count_train[6] = count_train[6] + 1
# if train[i] >= 70 and train[i] < 80:
# count_train[7] = count_train[7] + 1
# if train[i] >= 80 and train[i] < 90:
# count_train[8] = count_train[8] + 1
# if train[i] >= 90 and train[i] < 100:
# count_train[9] = count_train[9] + 1
# if train[i] >= 100 and train[i] < 110:
# count_train[10] = count_train[10] + 1
# return count_train
# count_train = [0 for i in range(11)]
# count_train = count_(train,count_train)
#
# count_test = [0 for i in range(11)]
# count_test = count_(test,count_test)
#
# count_val = [0 for i in range(11)]
# count_val = count_(val,count_val)
#
# print(count_train)
# print(count_test)
# print(count_val)
#
# def count_area(train,count_train):
# count_train = [0 for i in range(6)]
# for i in range(len(train)):
# if train[i]>=0 and train[i]<200 :
# count_train[0] = count_train[0] + 1
# if train[i] >= 200 and train[i] < 400:
# count_train[1] = count_train[1] + 1
# if train[i] >= 400 and train[i] < 600:
# count_train[2] = count_train[2] + 1
# if train[i] >= 600 and train[i] < 800:
# count_train[3] = count_train[3] + 1
# if train[i] >= 800 and train[i] < 1000:
# count_train[4] = count_train[4] + 1
# if train[i] >= 1000 :
# count_train[5] = count_train[5] + 1
# # if train[i] >= 800 and train[i] < 1000:
# # count_train[6] = count_train[6] + 1
# # if train[i] >= 1000 and train[i] < 2000:
# # count_train[7] = count_train[7] + 1
# # if train[i] >= 2000 and train[i] < 5000:
# # count_train[8] = count_train[8] + 1
# # if train[i] >= 5000 and train[i] < 10000:
# # count_train[9] = count_train[9] + 1
# # if train[i] >= 10000 :
# # count_train[10] = count_train[10] + 1
# return count_train
#
# random.shuffle(count_headarea) # 打乱列表顺序
#
# rate = 106374/3070
# print(rate)
#
# train_area = count_headarea[:round(1799*rate)]
# val_area = count_headarea[round(1800*rate):round(2099*rate)]
# test_area = count_headarea[round(2100*rate):]
#
# count_train = [0 for i in range(6)]
# count_train = count_area(train_area,count_train)
# #
# count_test = [0 for i in range(6)]
# count_test = count_area(test_area,count_test)
#
# count_val = [0 for i in range(6)]
# count_val = count_area(val_area,count_val)
#
# print(count_train)
# print(count_test)
# print(count_val)
mat = np.array(count_headarea)
print(np.argmin(mat))
print(x_min[np.argmin(mat)])
print(y_min[np.argmin(mat)])
print(x_max[np.argmin(mat)])
print(y_max[np.argmin(mat)])