生成train文件
import os
import os.path
path = "data/putao2/images/"
for filenames in os.walk(path):
filenames = list(filenames)
filenames = filenames[2]
for filename in filenames:
print(filename)
with open ("data/putao2/train.txt",'a') as f:
f.write(path+filename+'\n')
生成labels
# -*- coding: utf-8 -*-
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets = []
classes = ["disease"]
#原样保留。size为图片大小
# 将ROI的坐标转换为yolo需要的坐标
# size是图片的w和h
# box里保存的是ROI的坐标（x，y的最大值和最小值）
# 返回值为ROI中心点相对于图片大小的比例坐标，和ROI的w、h相对于图片大小的比例
def convert(size, box):
dw = 1./(size[0])
dh = 1./(size[1])
x = (box[0] + box[1])/2.0 - 1
y = (box[2] + box[3])/2.0 - 1
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(image_add):
#image_add进来的是带地址的.jpg
image_add = os.path.split(image_add)[1] #截取文件名带后缀
image_add = image_add[0:image_add.find('.',1)] #删除后缀，现在只有文件名没有后缀
print(image_add)
#现在传进来的只有图片名没有后缀
in_file = open('data/putao2/Annotations/' + image_add + '.xml')
out_file = open('data/putao2/labels/%s.txt'%(image_add), 'w')
tree=ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
#在一个XML中每个Object的迭代
for obj in root.iter('object'):
#iter()方法可以递归遍历元素/树的所有子元素
difficult = obj.find('difficult').text
#找到所有的椅子
cls = obj.find('name').text
#如果训练标签中的品种不在程序预定品种，或者difficult = 1，跳过此object
if cls not in classes or int(difficult)==1:
continue
#cls_id 只等于1
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
#b是每个Object中，一个bndbox上下左右像素的元组
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
bb = convert((w,h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
if not os.path.exists('data/putao2/labels/'):
os.makedirs('data/putao2/labels/')
image_adds = open("data/putao2/class_train1.txt")
for image_add in image_adds:
#print(image_add)
image_add = image_add.strip()
#print (image_add)
convert_annotation(image_add)
