输电线路缺陷检测是一个关键的任务，旨在确保电力传输系统的可靠性和安全性。今天我们使用CableInspect-AD数据集进行训练完成缺陷检测，下载的数据集格式如下：
我们需要经过以下步骤：
COCO转YOLO
首先是将COCO格式的数据转换为YOLO格式，即将json转换为txt，这里的json与我们先前的标注有所不同，其是把所有数据都存放到一个json文件中。
下面的脚本便是将所有的json转换为txt格式
import json
import os
from pathlib import Path
def coco_to_yolo(json_path, output_folder):
with open(json_path, 'r') as f:
data = json.load(f)
categories = {cat['id']: i for i, cat in enumerate(data['categories'])}
images = {img['id']: (img['file_name'], img['width'], img['height']) for img in data['images']}
output_path = Path(output_folder)
output_path.mkdir(parents=True, exist_ok=True)
for anno in data['annotations']:
img_id = anno['image_id']
if img_id not in images:
continue
file_name, img_w, img_h = images[img_id]
label_file = output_path / Path(file_name).with_suffix('.txt').name
class_id = categories[anno['category_id']]
x, y, w, h = anno['bbox']
# Normalize
xc = (x + w / 2) / img_w
yc = (y + h / 2) / img_h
wn = w / img_w
hn = h / img_h
with open(label_file, 'a') as f:
f.write(f"{class_id} {xc:.6f} {yc:.6f} {wn:.6f} {hn:.6f}\n")
print(f"✅ Labels saved to: {output_path}")
# 示例调用
coco_to_yolo("D:\datasets\CableInspect-AD\CableInspect-AD/cable_1.json", "labels")
coco_to_yolo("D:\datasets\CableInspect-AD\CableInspect-AD/cable_2.json", "labels")
coco_to_yolo("D:\datasets\CableInspect-AD\CableInspect-AD/cable_3.json", "labels")- 1.
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提取后的标签全部存储在labels文件夹里
同时我们还要确定有多少个类别，以及其对应的ID
import json
def print_categories(json_path):
with open(json_path, 'r') as f:
data = json.load(f)
categories = data.get('categories', [])
print(f"\n🔍 Categories in {json_path}:")
for cat in categories:
print(cat)
print_categories("D:/datasets/CableInspect-AD/CableInspect-AD/cable_1.json")- 1.
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数据集划分提取
得到txt标注文件后，我们就需要将数据集进行提取和划分，首先将图像全部提取到images文件夹里
import os
import shutil
def copy_jpg_files(src_dir, dst_dir):
# 如果目标文件夹不存在，则创建
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
# 遍历源文件夹及其所有子文件夹
for dirpath, _, filenames in os.walk(src_dir):
for filename in filenames:
if filename.lower().endswith('.png'):
# 构建完整的文件路径
file_path = os.path.join(dirpath, filename)
# 复制文件到目标文件夹
try:
shutil.copy(file_path, dst_dir)
print(f"已复制: {file_path}")
except Exception as e:
print(f"复制失败: {file_path} - {e}")
# 使用示例
source_directory = 'D:\datasets\CableInspect-AD\CableInspect-AD\Cable_3\images' # 替换为你的源文件夹路径
destination_directory = 'D:\datasets\images' # 替换为你想要放置.jpg文件的目标文件夹路径
copy_jpg_files(source_directory, destination_directory)- 1.
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随后，便根据labels提取对应的image，从而进行数据集划分：
import argparse
import json
import os
from tqdm import tqdm
import shutil
import random
def convert_label_json(json_dir, save_dir, classes):
json_paths = os.listdir(json_dir)
classes = classes.split(',')
# 创建输出文件夹
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for json_path in tqdm(json_paths):
# for json_path in json_paths:
path = os.path.join(json_dir, json_path)
encodings = ["utf-8", "gbk"]
for encoding in encodings:
try:
with open(path, "r", encoding=encoding) as load_f:
json_dict = json.load(load_f)
except (UnicodeDecodeError, json.JSONDecodeError):
continue
# try:
# with open(path, 'r',encoding="gbk") as load_f:
# json_dict = json.load(load_f)
# except TypeError:
# with open(path, 'r',encoding="utf-8") as load_f:
# json_dict = json.load(load_f)
# continue
h, w = json_dict['imageHeight'], json_dict['imageWidth']
# save txt path
txt_path = os.path.join(save_dir, json_path.replace('json', 'txt'))
txt_file = open(txt_path, 'w')
for shape_dict in json_dict['shapes']:
label = shape_dict['label']
label_index = classes.index(label)
points = shape_dict['points']
points_nor_list = []
for point in points:
points_nor_list.append(point[0] / w)
points_nor_list.append(point[1] / h)
points_nor_list = list(map(lambda x: str(x), points_nor_list))
points_nor_str = ' '.join(points_nor_list)
label_str = str(label_index) + ' ' + points_nor_str + '\n'
txt_file.writelines(label_str)
# 检查文件夹是否存在
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def split_datasets(image_dir, txt_dir, save_dir):
# 创建文件夹
mkdir(save_dir)
images_dir = os.path.join(save_dir, 'images')
labels_dir = os.path.join(save_dir, 'labels')
img_train_path = os.path.join(images_dir, 'train')
img_test_path = os.path.join(images_dir, 'test')
img_val_path = os.path.join(images_dir, 'val')
label_train_path = os.path.join(labels_dir, 'train')
label_test_path = os.path.join(labels_dir, 'test')
label_val_path = os.path.join(labels_dir, 'val')
mkdir(images_dir)
mkdir(labels_dir)
mkdir(img_train_path)
mkdir(img_test_path)
mkdir(img_val_path)
mkdir(label_train_path)
mkdir(label_test_path)
mkdir(label_val_path)
# 数据集划分比例，训练集75%，验证集15%，测试集15%，按需修改
train_percent = 0.8
val_percent = 0.2
test_percent = 0
total_txt = os.listdir(txt_dir)
num_txt = len(total_txt)
list_all_txt = range(num_txt) # 范围 range(0, num)
num_train = int(num_txt * train_percent)
num_val = int(num_txt * val_percent)
num_test = num_txt - num_train - num_val
train = random.sample(list_all_txt, num_train)
# 在全部数据集中取出train
val_test = [i for i in list_all_txt if not i in train]
# 再从val_test取出num_val个元素，val_test剩下的元素就是test
val = random.sample(val_test, num_val)
print("训练集数目：{}, 验证集数目：{},测试集数目：{}".format(len(train), len(val), len(val_test) - len(val)))
for i in list_all_txt:
name = total_txt[i][:-4]
srcImage = os.path.join(image_dir, name + '.png')
srcLabel = os.path.join(txt_dir, name + '.txt')
if i in train:
dst_train_Image = os.path.join(img_train_path, name + '.png')
dst_train_Label = os.path.join(label_train_path, name + '.txt')
shutil.copyfile(srcImage, dst_train_Image)
shutil.copyfile(srcLabel, dst_train_Label)
elif i in val:
dst_val_Image = os.path.join(img_val_path, name + '.png')
dst_val_Label = os.path.join(label_val_path, name + '.txt')
shutil.copyfile(srcImage, dst_val_Image)
shutil.copyfile(srcLabel, dst_val_Label)
else:
dst_test_Image = os.path.join(img_test_path, name + '.png')
dst_test_Label = os.path.join(label_test_path, name + '.txt')
shutil.copyfile(srcImage, dst_test_Image)
shutil.copyfile(srcLabel, dst_test_Label)
if __name__=="__main__":
# # labelme生成的json格式标注转为yolov8支持的txt格式
# """
# python json2txt_nomalize.py --json-dir my_datasets/color_rings/jsons --save-dir my_datasets/color_rings/txts --classes "cat,dogs"
# """
# parser = argparse.ArgumentParser(description='json convert to txt params')
# parser.add_argument('--json-dir', type=str,default='D:/project_mine/detection/datasets/juanzi_origin/labels', help='json path dir')
# parser.add_argument('--save-dir', type=str,default='D:/project_mine/detection/datasets/juanzi_origin/txt' ,help='txt save dir')
# parser.add_argument('--classes', type=str, default='bjyz',help='classes')#,道路,房屋,水渠,桥
# args = parser.parse_args()
# json_dir = args.json_dir
# save_dir = args.save_dir
# classes = args.classes
# convert_label_json(json_dir, save_dir, classes)
# 将图片和标注数据按比例切分为 训练集和测试集
"""
python split_datasets.py --image-dir my_datasets/color_rings/imgs --txt-dir my_datasets/color_rings/txts --save-dir my_datasets/color_rings/train_data
"""
parser = argparse.ArgumentParser(description='split datasets to train,val,test params')
parser.add_argument('--image-dir', type=str,default='D:\datasets\images', help='image path dir')
parser.add_argument('--txt-dir', type=str,default='D:\datasets\labels' , help='txt path dir')
parser.add_argument('--save-dir', default='D:/project_mine/detection/datasets/quexian',type=str, help='save dir')
args_split = parser.parse_args()
image_dir = args_split.image_dir
txt_dir = args_split.txt_dir
save_dir_split = args_split.save_dir
split_datasets(image_dir, txt_dir, save_dir_split)- 1.
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至此，我们就得到了YOLO格式的数据集了。
随后我们设置以下数据集配置文件和训练文件即可开启训练了
path: ../datasets/quexian # dataset root dir
train: images/train # train images (relative to 'path') 4 images
val: images/val # val images (relative to 'path') 4 images
test: # test images (optional)
# Classes
names:
0: broken strand
1: welded strand
2: bent strand
3: long scratch
4: crushed
5: spaced strand
6: deposit- 1.
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