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prepare_data.py
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prepare_data.py
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# -*- coding:utf-8 -*-
'''
@author: linxu
@contact: 17746071609@163.com
@time: 2021-11-24 12:56 PM
@desc: data prepare
'''
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile
from tqdm import tqdm
# 分类类别
classes = ["head", "person", "helmet"] # helmet detect
# 划分训练集比率
TRAIN_RATIO = 90
def clear_hidden_files(path):
'''
clean .DS_Store
:param path:
:return:
'''
dir_list = os.listdir(path)
for i in dir_list:
abspath = os.path.join(os.path.abspath(path), i)
if os.path.isfile(abspath):
if i.startswith("._"):
os.remove(abspath)
else:
clear_hidden_files(abspath)
def convert(size, box):
'''
corvert format
:param size:
:param box:
:return:
'''
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)
classlist = []
def convert_annotation(dir_path, dataset_name, image_id):
'''
转换annotation
:param image_id:
:return:
'''
in_file = open(dir_path + dataset_name + '/VOC2007/Annotations/%s.xml' % image_id)
out_file = open(dir_path + dataset_name + '/VOC2007/YOLOLabels/%s.txt' % image_id, '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)
for obj in root.iter('object'):
cls = obj.find('name').text
classlist.append(cls)
if len(classes) > 1:
difficult = obj.find('difficult').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))
# 避免由于w或h为0造成的convert带来的错误
if w != 0 and h != 0:
bb = convert((w, h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
# 整理object类别列表
classdd = list(set(classlist))
# print('classlist', classdd)
in_file.close()
out_file.close()
def trans_prepare_config(dir_path='data/', dataset_name='VOCdevkit_xxx'):
data_base_dir = os.path.join(dir_path + dataset_name + "/")
if not os.path.isdir(data_base_dir):
os.mkdir(data_base_dir)
print('data_base_dir', data_base_dir)
work_sapce_dir = os.path.join(data_base_dir, "VOC2007/")
if not os.path.isdir(work_sapce_dir):
os.mkdir(work_sapce_dir)
annotation_dir = os.path.join(work_sapce_dir, "Annotations/")
if not os.path.isdir(annotation_dir):
os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, "JPEGImages/")
if not os.path.isdir(image_dir):
os.mkdir(image_dir)
clear_hidden_files(image_dir)
yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")
if not os.path.isdir(yolo_labels_dir):
os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)
yolo_images_dir = os.path.join(data_base_dir, "images/")
if not os.path.isdir(yolo_images_dir):
os.mkdir(yolo_images_dir)
clear_hidden_files(yolo_images_dir)
yolo_labels_dir = os.path.join(data_base_dir, "labels/")
if not os.path.isdir(yolo_labels_dir):
os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)
yolo_images_train_dir = os.path.join(yolo_images_dir, "train/")
if not os.path.isdir(yolo_images_train_dir):
os.mkdir(yolo_images_train_dir)
clear_hidden_files(yolo_images_train_dir)
yolo_images_test_dir = os.path.join(yolo_images_dir, "val/")
if not os.path.isdir(yolo_images_test_dir):
os.mkdir(yolo_images_test_dir)
clear_hidden_files(yolo_images_test_dir)
yolo_labels_train_dir = os.path.join(yolo_labels_dir, "train/")
if not os.path.isdir(yolo_labels_train_dir):
os.mkdir(yolo_labels_train_dir)
clear_hidden_files(yolo_labels_train_dir)
yolo_labels_test_dir = os.path.join(yolo_labels_dir, "val/")
if not os.path.isdir(yolo_labels_test_dir):
os.mkdir(yolo_labels_test_dir)
clear_hidden_files(yolo_labels_test_dir)
print('dir_path', dir_path)
train_file = open(dir_path + "yolov5_train.txt", 'w')
test_file = open(dir_path + "yolov5_val.txt", 'w')
train_file.close()
test_file.close()
train_file = open(os.path.join(dir_path + "yolov5_train.txt"), 'a')
test_file = open(os.path.join(dir_path + "yolov5_val.txt"), 'a')
list_imgs = os.listdir(image_dir) # list image files
prob = random.randint(1, 100)
# for i in range(0, len(list_imgs)):
for i in tqdm(range(0, len(list_imgs))):
path = os.path.join(image_dir, list_imgs[i])
if os.path.isfile(path):
image_path = image_dir + list_imgs[i]
voc_path = list_imgs[i]
(nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
(voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
annotation_name = nameWithoutExtention + '.xml'
annotation_path = os.path.join(annotation_dir, annotation_name)
label_name = nameWithoutExtention + '.txt'
label_path = os.path.join(yolo_labels_dir, label_name)
prob = random.randint(1, 100)
print('file:', annotation_name, '|', "Probability: %d" % prob)
# train dataset
if (prob < TRAIN_RATIO):
if os.path.exists(annotation_path):
train_file.write(image_path + '\n')
# 转换label
# print('nameWithoutExtention',nameWithoutExtention)
convert_annotation(dir_path=dir_path, dataset_name=dataset_name, image_id=nameWithoutExtention)
copyfile(image_path, yolo_images_train_dir + voc_path)
copyfile(label_path, yolo_labels_train_dir + label_name)
else:
# val
if os.path.exists(annotation_path):
test_file.write(image_path + '\n')
# 转换label
convert_annotation(dir_path=dir_path, dataset_name=dataset_name, image_id=nameWithoutExtention)
copyfile(image_path, yolo_images_test_dir + voc_path)
copyfile(label_path, yolo_labels_test_dir + label_name)
print('classlist', classlist)
train_file.close()
test_file.close()
if __name__ == '__main__':
# dataset_root_dir
dir_path = '/home/linxu/Desktop/datasetsHUb/'
# dataset_name
dataset_name = 'dataset_helmet'
trans_prepare_config(dir_path, dataset_name)