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"""
3.生成训练集和测试集及其名称
"""
import xml.etree.ElementTree as ET
import pickle
import os
import shutil
from os import listdir, getcwd
from os.path import join
sets = ['train', 'val']
classes = ['ignored regions', 'pedestrian', 'people', 'bicycle', 'car', 'van',
'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor', 'others']
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0
y = (box[2] + box[3]) / 2.0
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_id):
in_file = open('data/all_xml/%s.xml' % (image_id), encoding='utf-8')
out_file = open('data/all_labels/%s.txt' % (image_id), 'w', encoding='utf-8')
tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
"""b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))"""
"""
防止xml文件中min值比max值大，导致txt文件中标记数据出现负值的情况
"""
b = [float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text)]
if b[0] > b[1]:
b[0], b[1] = b[1], b[0]
if b[2] > b[3]:
b[2], b[3] = b[3], b[2]
b = tuple(b)
bb = convert((w, h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
print(wd)
for image_set in sets:
if not os.path.exists('data/all_labels/'):
os.makedirs('data/all_labels/')
image_ids = open('data/ImageSets/%s.txt' % (image_set), encoding='utf-8').read().strip().split()
image_list_file = open('data/images_%s.txt' % (image_set), 'w', encoding='utf-8')
labels_list_file = open('data/labels_%s.txt' % (image_set), 'w', encoding='utf-8')
for image_id in image_ids:
image_list_file.write('%s.jpg\n' % (image_id))
labels_list_file.write('%s.txt\n' % (image_id))
convert_annotation(image_id) # 如果标签已经是txt格式，将此行注释掉，所有的txt存放到all_labels文件夹。
image_list_file.close()
labels_list_file.close()
def copy_file(new_path, path_txt, search_path): # 参数1：存放新文件的位置 参数2：为上一步建立好的train,val训练数据的路径txt文件 参数3：为搜索的文件位置
if not os.path.exists(new_path):
os.makedirs(new_path)
with open(path_txt, 'r') as lines:
filenames_to_copy = set(line.rstrip() for line in lines)
# print('filenames_to_copy:',filenames_to_copy)
# print(len(filenames_to_copy))
for root, _, filenames in os.walk(search_path):
# print('root',root)
# print(_)
# print(filenames)
for filename in filenames:
if filename in filenames_to_copy:
shutil.copy(os.path.join(root, filename), new_path)
# 按照划分好的训练文件的路径搜索目标，并将其复制到yolo格式下的新路径
copy_file('./data/images/train/', './data/images_train.txt', './data/all_images')
copy_file('./data/images/val/', './data/images_val.txt', './data/all_images')
copy_file('./data/labels/train/', './data/labels_train.txt', './data/all_labels')
copy_file('./data/labels/val/', './data/labels_val.txt', './data/all_labels')
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