YOLO训练自己的数据集 | 格式转换 | 未完待续...
场景1.将JSON文件转换为TXT文件,并按照比例划分训练集、验证集、测试集☀需求分析转换步骤step 1.将JSON文件转换为XML文件step 2.将XML文件转换为TXT文件，并按照比例划分训练集、测试集、验证集
------------------------------------------------------------------------------------------场景2.将JSON文件直接转换成TXT文件☀需求分析转换步骤
------------------------------------------------------------------------------------------场景3.将TXT文件直接转换成JSON文件☀需求分析转换步骤
------------------------------------------------------------------------------------------场景4.将TXT文件直接转换成XML文件☀需求分析转换步骤
场景1.将JSON文件转换为TXT文件,并按照比例划分训练集、验证集、测试集☀
需求分析
当我们在使用了Labelme等一系列数据标注软件后，便会得到与图片对应的JSON文件，但我们在训练YOLO模型之前，通常需要将标注数据转换为YOLO能识别的格式，也即TXT文件格式。再用于训练模型。而TXT格式是一种简单易读的文本格式，可以方便地手动修改标签中的信息，例如类别名称、边界框坐标等。将标签转换为TXT格式是为了方便后续的YOLO训练和测试，并提高工作效率。
转换步骤
step 1.将JSON文件转换为XML文件
创建create_xml_anno.py文件
from xml.dom.minidom import Document
class CreateAnno:
def __init__(self, ):
self.doc = Document() # 创建DOM文档对象
self.anno = self.doc.createElement('annotation') # 创建根元素
self.doc.appendChild(self.anno)
self.add_folder()
self.add_path()
self.add_source()
self.add_segmented()
# self.add_filename()
# self.add_pic_size(width_text_str=str(width), height_text_str=str(height), depth_text_str=str(depth))
def add_folder(self, floder_text_str='JPEGImages'):
floder = self.doc.createElement('floder') ##建立自己的开头
floder_text = self.doc.createTextNode(floder_text_str) ##建立自己的文本信息
floder.appendChild(floder_text) ##自己的内容
self.anno.appendChild(floder)
def add_filename(self, filename_text_str='00000.jpg'):
filename = self.doc.createElement('filename')
filename_text = self.doc.createTextNode(filename_text_str)
filename.appendChild(filename_text)
self.anno.appendChild(filename)
def add_path(self, path_text_str="None"):
path = self.doc.createElement('path')
path_text = self.doc.createTextNode(path_text_str)
path.appendChild(path_text)
self.anno.appendChild(path)
def add_source(self, database_text_str="Unknow"):
source = self.doc.createElement('source')
database = self.doc.createElement('database')
database_text = self.doc.createTextNode(database_text_str) # 元素内容写入
database.appendChild(database_text)
source.appendChild(database)
self.anno.appendChild(source)
def add_pic_size(self, width_text_str="0", height_text_str="0", depth_text_str="3"):
size = self.doc.createElement('size')
width = self.doc.createElement('width')
width_text = self.doc.createTextNode(width_text_str) # 元素内容写入
width.appendChild(width_text)
size.appendChild(width)
height = self.doc.createElement('height')
height_text = self.doc.createTextNode(height_text_str)
height.appendChild(height_text)
size.appendChild(height)
depth = self.doc.createElement('depth')
depth_text = self.doc.createTextNode(depth_text_str)
depth.appendChild(depth_text)
size.appendChild(depth)
self.anno.appendChild(size)
def add_segmented(self, segmented_text_str="0"):
segmented = self.doc.createElement('segmented')
segmented_text = self.doc.createTextNode(segmented_text_str)
segmented.appendChild(segmented_text)
self.anno.appendChild(segmented)
def add_object(self,
name_text_str="None",
xmin_text_str="0",
ymin_text_str="0",
xmax_text_str="0",
ymax_text_str="0",
pose_text_str="Unspecified",
truncated_text_str="0",
difficult_text_str="0"):
object = self.doc.createElement('object')
name = self.doc.createElement('name')
name_text = self.doc.createTextNode(name_text_str)
name.appendChild(name_text)
object.appendChild(name)
pose = self.doc.createElement('pose')
pose_text = self.doc.createTextNode(pose_text_str)
pose.appendChild(pose_text)
object.appendChild(pose)
truncated = self.doc.createElement('truncated')
truncated_text = self.doc.createTextNode(truncated_text_str)
truncated.appendChild(truncated_text)
object.appendChild(truncated)
difficult = self.doc.createElement('difficult')
difficult_text = self.doc.createTextNode(difficult_text_str)
difficult.appendChild(difficult_text)
object.appendChild(difficult)
bndbox = self.doc.createElement('bndbox')
xmin = self.doc.createElement('xmin')
xmin_text = self.doc.createTextNode(xmin_text_str)
xmin.appendChild(xmin_text)
bndbox.appendChild(xmin)
ymin = self.doc.createElement('ymin')
ymin_text = self.doc.createTextNode(ymin_text_str)
ymin.appendChild(ymin_text)
bndbox.appendChild(ymin)
xmax = self.doc.createElement('xmax')
xmax_text = self.doc.createTextNode(xmax_text_str)
xmax.appendChild(xmax_text)
bndbox.appendChild(xmax)
ymax = self.doc.createElement('ymax')
ymax_text = self.doc.createTextNode(ymax_text_str)
ymax.appendChild(ymax_text)
bndbox.appendChild(ymax)
object.appendChild(bndbox)
self.anno.appendChild(object)
def get_anno(self):
return self.anno
def get_doc(self):
return self.doc
def save_doc(self, save_path):
with open(save_path, "w") as f:
self.doc.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')
再创建read_json_anno.py文件
import numpy as np
import json
class ReadAnno:
def __init__(self, json_path, process_mode="rectangle"):
self.json_data = json.load(open(json_path))
self.filename = self.json_data['imagePath']
self.width = self.json_data['imageWidth']
self.height = self.json_data['imageHeight']
self.coordis = []
assert process_mode in ["rectangle", "polygon"]
if process_mode == "rectangle":
self.process_polygon_shapes()
elif process_mode == "polygon":
self.process_polygon_shapes()
def process_rectangle_shapes(self):
for single_shape in self.json_data['shapes']:
bbox_class = single_shape['label']
xmin = single_shape['points'][0][0]
ymin = single_shape['points'][0][1]
xmax = single_shape['points'][1][0]
ymax = single_shape['points'][1][1]
self.coordis.append([xmin, ymin, xmax, ymax, bbox_class])
def process_polygon_shapes(self):
for single_shape in self.json_data['shapes']:
bbox_class = single_shape['label']
temp_points = []
for couple_point in single_shape['points']:
x = float(couple_point[0])
y = float(couple_point[1])
temp_points.append([x, y])
temp_points = np.array(temp_points)
xmin, ymin = temp_points.min(axis=0)
xmax, ymax = temp_points.max(axis=0)
self.coordis.append([xmin, ymin, xmax, ymax, bbox_class])
def get_width_height(self):
return self.width, self.height
def get_filename(self):
return self.filename
def get_coordis(self):
return self.coordis
最后创建main.py文件，需要修改root_json_dir、root_save_xml_dir
import os
from tqdm import tqdm
from read_json_anno import ReadAnno
from create_xml_anno import CreateAnno
def json_transform_xml(json_path, xml_path, process_mode="rectangle"):
json_path = json_path
json_anno = ReadAnno(json_path, process_mode=process_mode)
width, height = json_anno.get_width_height()
filename = json_anno.get_filename()
coordis = json_anno.get_coordis()
xml_anno = CreateAnno()
xml_anno.add_filename(filename)
xml_anno.add_pic_size(width_text_str=str(width), height_text_str=str(height), depth_text_str=str(3))
for xmin, ymin, xmax, ymax, label in coordis:
xml_anno.add_object(name_text_str=str(label),
xmin_text_str=str(int(xmin)),
ymin_text_str=str(int(ymin)),
xmax_text_str=str(int(xmax)),
ymax_text_str=str(int(ymax)))
xml_anno.save_doc(xml_path)
if __name__ == "__main__":
root_json_dir = r"放存放JSON文件的路径"
root_save_xml_dir = r"放即将生成的XML文件的保存路径"
for json_filename in tqdm(os.listdir(root_json_dir)):
if json_filename.split('.')[-1]=='json':
json_path = os.path.join(root_json_dir, json_filename)
save_xml_path = os.path.join(root_save_xml_dir, json_filename.replace(".json", ".xml"))
json_transform_xml(json_path, save_xml_path, process_mode="polygon") # labelme原数据的标注方式(矩形rectangle和多边形polygon)
step 2.将XML文件转换为TXT文件，并按照比例划分训练集、测试集、验证集
创建xml_to_txt.py文件
import os
import shutil
import random
# 保证随机可复现
random.seed(0)
def split_data(file_path, new_file_path, train_rate, val_rate, test_rate):
eachclass_image = []
for image in os.listdir(file_path):
eachclass_image.append(image)
total = len(eachclass_image)
random.shuffle(eachclass_image)
train_images = eachclass_image[0:int(train_rate * total)] # 注意左闭右开
val_images = eachclass_image[int(train_rate * total):int((train_rate + val_rate) * total)] # 注意左闭右开
test_images = eachclass_image[int((train_rate + val_rate) * total):]
# 训练集
for image in train_images:
print(image)
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'train' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
# print(new_path)
shutil.copy(old_path, new_path)
new_name = os.listdir(new_file_path + '/' + 'train' + '/' + 'images')
# print(new_name[1][:-4])
for im in new_name:
old_xmlpath = xmlpath + '/' + im[:-3] + 'txt'
print('old',old_xmlpath)
new_xmlpath1 = new_file_path + '/' + 'train' + '/' + 'labels'
if not os.path.exists(new_xmlpath1):
os.makedirs(new_xmlpath1)
new_xmlpath = new_xmlpath1 + '/' + im[:-3] + 'txt'
print('xml name',new_xmlpath)
if not os.path.exists(f'{old_xmlpath}'):
open(f'{old_xmlpath}', 'w')
shutil.copy(old_xmlpath, new_xmlpath)
# 验证集
for image in val_images:
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'val' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
new_name = os.listdir(new_file_path + '/' + 'val' + '/' + 'images')
for im in new_name:
old_xmlpath = xmlpath + '/' + im[:-3] + 'txt'
new_xmlpath1 = new_file_path + '/' + 'val' + '/' + 'labels'
if not os.path.exists(new_xmlpath1):
os.makedirs(new_xmlpath1)
new_xmlpath = new_xmlpath1 + '/' + im[:-3] + 'txt'
if not os.path.exists(f'{old_xmlpath}'):
open(f'{old_xmlpath}', 'w')
shutil.copy(old_xmlpath, new_xmlpath)
#测试集
for image in test_images:
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'test' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
new_name = os.listdir(new_file_path + '/' + 'test' + '/' + 'images')
for im in new_name:
old_xmlpath = xmlpath + '/' + im[:-3] + 'txt'
new_xmlpath1 = new_file_path + '/' + 'test' + '/' + 'labels'
if not os.path.exists(new_xmlpath1):
os.makedirs(new_xmlpath1)
new_xmlpath = new_xmlpath1 + '/' + im[:-3] + 'txt'
if not os.path.exists(f'{old_xmlpath}'):
open(f'{old_xmlpath}', 'w')
shutil.copy(old_xmlpath, new_xmlpath)
print('ok')
if __name__ == '__main__':
file_path = "存放图片的文件夹路径//"
xmlpath = '存放xml文件的路径//'
new_file_path = "存放生成数据集的路径//"
split_data(file_path, new_file_path, train_rate=0.8, val_rate=0.1, test_rate=0.1)#这里是划分比例，可自己调节，一般是8：1：1，也有7：2：1的划分比例情况没具体划分情况看数据集的大小
运行xml_to_txt.py文件后看到这样的格式，也就代表成功啦
------------------------------------------------------------------------------------------
场景2.将JSON文件直接转换成TXT文件☀
需求分析
有的小伙伴再使用了Labelme这种标注软件之后，想直接把JSON文件转换为TXT文件，当然这也是可以的，废话不多说，直接上代码
转换步骤
创建json_to_txt.py文件，需要修改txt_name、json_floder_path
import json
import os
import pandas as pd
def convert(img_size, box):
x1 = box[0]
y1 = box[1]
x2 = box[2]
y2 = box[3]
return (x1, y1, x2, y2)
def decode_json(json_floder_path, json_name,label):
txt_name = r'这里存放即将生成txt文件的路径/' + json_name[0:-5] + '.txt'
txt_file = open(txt_name, 'w')
json_path = os.path.join(json_floder_path, json_name)
data = json.load(open(json_path, 'r'))
img_w = data['imageWidth']
img_h = data['imageHeight']
for i in data['shapes']:
if i['shape_type'] == 'rectangle':
if (label['label'] != i['label']).all():
new_label=pd.DataFrame(columns=['label'], data=[i['label']])
label=label.append(new_label,ignore_index=True)
try:
x1 = float((i['points'][0][0])) / img_w
y1 = float((i['points'][0][1])) / img_h
x2 = float((i['points'][1][0])) / img_w
y2 = float((i['points'][1][1])) / img_h
n = label[label['label']==i['label']].index[0]
bb = (x1, y1, x2, y2)
bbox = convert((img_w, img_h), bb)
txt_file.write(str(n) + " " + " ".join([str(a) for a in bbox]) + '\n')
except IndexError:
print(json_name[0:-5]+'的'+i['label']+"标签坐标缺失")
return label
if __name__ == "__main__":
json_floder_path = r'这里存放你存json文件的路径/'
json_names = os.listdir(json_floder_path)
label= pd.DataFrame(columns = ['label'])
for json_name in json_names:
if json_name[-4:]=='json':
print(json_name)
label=decode_json(json_floder_path, json_name,label)
label.to_csv('label.txt', sep='\t', index=True)
------------------------------------------------------------------------------------------
场景3.将TXT文件直接转换成JSON文件☀
需求分析
有的小伙伴在将JSON文件转换为TXT文件后，就把JSON文件给删除了，但是后续想要对图像进行再次标注的时候，无法找到原始的JSON数据，产生了想要重新标注的危险想法，以下代码可实现对TXT文件直接转换为JSON文件的需求，废话不多说，直接上代码
转换步骤
创建txt_to_json.py文件,这里假设需要数据集有两类，分别为dog和cat,如果你的标签名是别的，那么就需要修改class_name,你将修改的是txt_folder、output_folder，img_folder。
import os
import json
import base64
import cv2
def read_txt_file(txt_file):
with open(txt_file, 'r') as f:
lines = f.readlines()
data = []
for line in lines:
line = line.strip().split()
class_name = line[0]
bbox = [coord for coord in line[1:]]
data.append({'class_name': class_name, 'bbox': bbox})
return data
def convert_to_labelme(data, image_path, image_size):
labelme_data = {
'version': '4.5.6',
'flags': {},
'shapes': [],
'imagePath': json_image_path,
'imageData': None,
'imageHeight': image_size[0],
'imageWidth': image_size[1]
}
for obj in data:
dx = obj['bbox'][0]
dy = obj['bbox'][1]
dw = obj['bbox'][2]
dh = obj['bbox'][3]
w = eval(dw) * image_size[1]
h = eval(dh) * image_size[0]
center_x = eval(dx) * image_size[1]
center_y = eval(dy) * image_size[0]
x1 = center_x - w/2
y1 = center_y - h/2
x2 = center_x + w/2
y2 = center_y + h/2
if obj['dog'] == '0': #判断对应的标签名称，写入json文件中
label = str('grape')
else:
label = obj['cat']
shape_data = {
'label': label,
'points': [[x1, y1], [x2, y2]],
'group_id': None,
'shape_type': 'rectangle',
'flags': {}
}
labelme_data['shapes'].append(shape_data)
return labelme_data
def save_labelme_json(labelme_data, image_path, output_file):
with open(image_path, 'rb') as f:
image_data = f.read()
labelme_data['imageData'] = base64.b64encode(image_data).decode('utf-8')
with open(output_file, 'w') as f:
json.dump(labelme_data, f, indent=4)
# 设置文件夹路径和输出文件夹路径
txt_folder = r"存放txt文件的文件夹路径//"
output_folder = r"输出json文件的文件夹路径//"
img_folder = r"存放对应标签的图片文件夹路径//"
# 创建输出文件夹
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# 遍历txt文件夹中的所有文件
for filename in os.listdir(txt_folder):
if filename.endswith('.txt'):
# 生成对应的输出文件名
output_filename = os.path.splitext(filename)[0] + '.json'
# 读取txt文件
txt_file = os.path.join(txt_folder, filename)
data = read_txt_file(txt_file)
# 设置图片路径和尺寸
image_filename = os.path.splitext(filename)[0] + '.jpg' # 图片文件名与txt文件名相同，后缀为.jpg
image_path = os.path.join(img_folder, image_filename)
# image_size = (1280, 720) # 根据实际情况修改
json_image_path = image_path.split('\\')[-1]
print("image_path:", image_path)
image_size = cv2.imread(image_path).shape
# 转化为LabelMe格式
labelme_data = convert_to_labelme(data, image_path, image_size)
# 保存为LabelMe JSON文件
output_file = os.path.join(output_folder, output_filename)
save_labelme_json(labelme_data, image_path, output_file)
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场景4.将TXT文件直接转换成XML文件☀
需求分析
当我们需要对数据集利用imgaug库进行数据增强时，或者需要对锚框重聚类分析，包括但不限于kmeans聚类、kmeans++聚类、kmeans聚类融合遗传算法等聚类方法时，这个时候就需要使用到我们XML格式的文件了，但是有的小伙伴没有保存此类文件，仅有TXT文件时，我们可以利用TXT文件转为XML文件。
转换步骤
待更新...
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