1.利用labelme进行数据标注
1.1Labelme 安装方法
首先安装 Anaconda,然后运行下列命令:
##################
## for Python 2 ##
##################
conda create --name=labelme python=2.7
source activate labelme
# conda install -c conda-forge pyside2
conda install pyqt
pip install labelme
# 如果想安装最新版本,请使用下列命令安装:
# pip install git+https://github.com/wkentaro/labelme.git
##################
## for Python 3 ##
##################
conda create --name=labelme python=3.6
source activate labelme
# conda install -c conda-forge pyside2
# conda install pyqt
pip install pyqt5 # pyqt5 can be installed via pip on python3
pip install labelme
输入以下指令打开
labelme
1.2Labelme 使用教程
使用 labelme 进行场景分割标注的教程详见:labelme
2.转换划分数据集
对数据集进行转换和划分。注意:在数据标注的时候将图片和json文件放在不同的文件夹里。如下图所示,另外新建两个文件夹txt 和split。
2.1将json格式文件转换为txt格式
新建json2txt.py文件,修改文件路径为自己的路径
# -*- coding: utf-8 -*-
import json
import os
import argparse
from tqdm import tqdm
def convert_label_json(json_dir, save_dir, classes):
json_paths = os.listdir(json_dir)
classes = classes.split(',')
for json_path in tqdm(json_paths):
# for json_path in json_paths:
path = os.path.join(json_dir, json_path)
with open(path, 'r') as load_f:
json_dict = json.load(load_f)
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)
if __name__ == "__main__":
"""
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:/ultralytics-main/data/json', help='json path dir')
parser.add_argument('--save-dir', type=str,default='D:/ultralytics-main/data/txt' ,help='txt save dir')
parser.add_argument('--classes', type=str, default='ccc,ccc1',help='classes')
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)
2.2划分数据集
新建split.py,修改文件路径为自己的路径
# 将图片和标注数据按比例切分为 训练集和测试集
import shutil
import random
import os
import argparse
# 检查文件夹是否存在
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def main(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 = 0.1
test_percent = 0.1
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 + '.jpg')
srcLabel = os.path.join(txt_dir, name + '.txt')
if i in train:
dst_train_Image = os.path.join(img_train_path, name + '.jpg')
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 + '.jpg')
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 + '.jpg')
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__':
"""
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:/ultralytics-main/data', help='image path dir')
parser.add_argument('--txt-dir', type=str,default='D:/ultralytics-main/data/txt' , help='txt path dir')
parser.add_argument('--save-dir', default='D:/ultralytics-main/data/split',type=str, help='save dir')
args = parser.parse_args()
image_dir = args.image_dir
txt_dir = args.txt_dir
save_dir = args.save_dir
main(image_dir, txt_dir, save_dir)
运行完后得到如下文件
3.训练设置
3.1新建seg.yaml文件 ,按照下列格式创建 我一般写成绝对路径,方便一点。
train: D:\ultralytics-main\data\split\images\train # train images (relative to 'path') 128 images
val: D:\ultralytics-main\data\split\images\val # val images (relative to 'path') 128 images
test: D:\ultralytics-main\data\split\images\test # test images (optional)
# Classes
names:
0: ccc
1: ccc1
3.2训练参数设置
task: segment # YOLO task, i.e. detect, segment, classify, pose
mode: train # YOLO mode, i.e. train, val, predict, export, track, benchmark
# Train settings -------------------------------------------------------------------------------------------------------
model: yolov8s-seg.yaml # path to model file, i.e. yolov8n.pt, yolov8n.yaml
#model:runs/detect/yolov8s/weights/best.pt
data: seg.yaml # path to data file, i.e. coco128.yaml
epochs: 10 # number of epochs to train for
patience: 50 # epochs to wait for no observable improvement for early stopping of training
batch: 16 # number of images per batch (-1 for AutoBatch)
然后开始训练即可。
参考:
(52条消息) 数据标注软件labelme详解_黑暗星球的博客-CSDN博客文章来源:https://www.toymoban.com/news/detail-430534.html
(52条消息) YOLOv5-7.0实例分割训练自己的数据,切分mask图并摆正_yolo 图像分割_jin__9981的博客-CSDN博客文章来源地址https://www.toymoban.com/news/detail-430534.html
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