数据集准备
数据集标注参考博客【使用labelimg制作数据集】:https://blog.csdn.net/qq_41251963/article/details/111190442
标注数据注意事项,图片名称为纯数字,例如1289.jpg ;不要出现其他字符,否则下面代码转换会报错。
标注好的数据集格式为VOC格式,AI Studio 中PPYOLOE用到的数据格式为coco数据格式,需要将标注好的数据进行格式转换。执行python voc2coco.py 即可!转换代码如下:
voc2coco.py
import os
import random
import shutil
import sys
import json
import glob
import xml.etree.ElementTree as ET
"""
代码来源:https://github.com/Stephenfang51/VOC_to_COCO
You only need to set the following three parts
1.val_files_num : num of validation samples from your all samples
2.test_files_num = num of test samples from your all samples
3.voc_annotations : path to your VOC dataset Annotations(最好写成绝对路径)
"""
val_files_num = 0
test_files_num = 0
voc_annotations = r'C:/Users/liq/Desktop/VOC/Annotations/' #remember to modify the path
split = voc_annotations.split('/')
coco_name = split[-3]
del split[-3]
del split[-2]
del split[-1]
del split[0]
# print(split)
main_path = ''
for i in split:
main_path += '/' + i
main_path = main_path + '/'
# print(main_path)
coco_path = os.path.join(main_path, coco_name+'_COCO/')
coco_images = os.path.join(main_path, coco_name+'_COCO/images')
coco_json_annotations = os.path.join(main_path, coco_name+'_COCO/annotations/')
xml_val = os.path.join(main_path, 'xml', 'xml_val/')
xml_test = os.path.join(main_path, 'xml/', 'xml_test/')
xml_train = os.path.join(main_path, 'xml/', 'xml_train/')
voc_images = os.path.join(main_path, coco_name, 'JPEGImages/')
#from https://www.php.cn/python-tutorials-424348.html
def mkdir(path):
path=path.strip()
path=path.rstrip("\\")
isExists=os.path.exists(path)
if not isExists:
os.makedirs(path)
print(path+' ----- folder created')
return True
else:
print(path+' ----- folder existed')
return False
#foler to make, please enter full path
mkdir(coco_path)
mkdir(coco_images)
mkdir(coco_json_annotations)
mkdir(xml_val)
mkdir(xml_test)
mkdir(xml_train)
#voc images copy to coco images
for i in os.listdir(voc_images):
img_path = os.path.join(voc_images + i)
shutil.copy(img_path, coco_images)
# voc images copy to coco images
for i in os.listdir(voc_annotations):
img_path = os.path.join(voc_annotations + i)
shutil.copy(img_path, xml_train)
print("\n\n %s files copied to %s" % (val_files_num, xml_val))
for i in range(val_files_num):
if len(os.listdir(xml_train)) > 0:
random_file = random.choice(os.listdir(xml_train))
# print("%d) %s"%(i+1,random_file))
source_file = "%s/%s" % (xml_train, random_file)
if random_file not in os.listdir(xml_val):
shutil.move(source_file, xml_val)
else:
random_file = random.choice(os.listdir(xml_train))
source_file = "%s/%s" % (xml_train, random_file)
shutil.move(source_file, xml_val)
else:
print('The folders are empty, please make sure there are enough %d file to move' % (val_files_num))
break
for i in range(test_files_num):
if len(os.listdir(xml_train)) > 0:
random_file = random.choice(os.listdir(xml_train))
# print("%d) %s"%(i+1,random_file))
source_file = "%s/%s" % (xml_train, random_file)
if random_file not in os.listdir(xml_test):
shutil.move(source_file, xml_test)
else:
random_file = random.choice(os.listdir(xml_train))
source_file = "%s/%s" % (xml_train, random_file)
shutil.move(source_file, xml_test)
else:
print('The folders are empty, please make sure there are enough %d file to move' % (val_files_num))
break
print("\n\n" + "*" * 27 + "[ Done ! Go check your file ]" + "*" * 28)
# !/usr/bin/python
# pip install lxml
START_BOUNDING_BOX_ID = 1
PRE_DEFINE_CATEGORIES = None
# If necessary, pre-define category and its id
# PRE_DEFINE_CATEGORIES = {"aeroplane": 1, "bicycle": 2, "bird": 3, "boat": 4,
# "bottle":5, "bus": 6, "car": 7, "cat": 8, "chair": 9,
# "cow": 10, "diningtable": 11, "dog": 12, "horse": 13,
# "motorbike": 14, "person": 15, "pottedplant": 16,
# "sheep": 17, "sofa": 18, "train": 19, "tvmonitor": 20}
"""
main code below are from
https://github.com/Tony607/voc2coco
"""
def get(root, name):
vars = root.findall(name)
return vars
def get_and_check(root, name, length):
vars = root.findall(name)
if len(vars) == 0:
raise ValueError("Can not find %s in %s." % (name, root.tag))
if length > 0 and len(vars) != length:
raise ValueError(
"The size of %s is supposed to be %d, but is %d."
% (name, length, len(vars))
)
if length == 1:
vars = vars[0]
return vars
def get_filename_as_int(filename):
try:
filename = filename.replace("\\", "/")
filename = os.path.splitext(os.path.basename(filename))[0]
return int(filename)
except:
raise ValueError("Filename %s is supposed to be an integer." % (filename))
def get_categories(xml_files):
"""Generate category name to id mapping from a list of xml files.
Arguments:
xml_files {list} -- A list of xml file paths.
Returns:
dict -- category name to id mapping.
"""
classes_names = []
for xml_file in xml_files:
tree = ET.parse(xml_file)
root = tree.getroot()
for member in root.findall("object"):
classes_names.append(member[0].text)
classes_names = list(set(classes_names))
classes_names.sort()
return {name: i for i, name in enumerate(classes_names)}
def convert(xml_files, json_file):
json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []}
if PRE_DEFINE_CATEGORIES is not None:
categories = PRE_DEFINE_CATEGORIES
else:
categories = get_categories(xml_files)
bnd_id = START_BOUNDING_BOX_ID
for xml_file in xml_files:
tree = ET.parse(xml_file)
root = tree.getroot()
path = get(root, "path")
if len(path) == 1:
filename = os.path.basename(path[0].text)
elif len(path) == 0:
filename = get_and_check(root, "filename", 1).text
else:
raise ValueError("%d paths found in %s" % (len(path), xml_file))
## The filename must be a number
image_id = get_filename_as_int(filename)
size = get_and_check(root, "size", 1)
width = int(get_and_check(size, "width", 1).text)
height = int(get_and_check(size, "height", 1).text)
image = {
"file_name": filename,
"height": height,
"width": width,
"id": image_id,
}
json_dict["images"].append(image)
## Currently we do not support segmentation.
# segmented = get_and_check(root, 'segmented', 1).text
# assert segmented == '0'
for obj in get(root, "object"):
category = get_and_check(obj, "name", 1).text
if category not in categories:
new_id = len(categories)
categories[category] = new_id
category_id = categories[category]
bndbox = get_and_check(obj, "bndbox", 1)
xmin = int(get_and_check(bndbox, "xmin", 1).text) - 1
ymin = int(get_and_check(bndbox, "ymin", 1).text) - 1
xmax = int(get_and_check(bndbox, "xmax", 1).text)
ymax = int(get_and_check(bndbox, "ymax", 1).text)
assert xmax > xmin
assert ymax > ymin
o_width = abs(xmax - xmin)
o_height = abs(ymax - ymin)
ann = {
"area": o_width * o_height,
"iscrowd": 0,
"image_id": image_id,
"bbox": [xmin, ymin, o_width, o_height],
"category_id": category_id,
"id": bnd_id,
"ignore": 0,
"segmentation": [],
}
json_dict["annotations"].append(ann)
bnd_id = bnd_id + 1
for cate, cid in categories.items():
cat = {"supercategory": "none", "id": cid, "name": cate}
json_dict["categories"].append(cat)
os.makedirs(os.path.dirname(json_file), exist_ok=True)
json_fp = open(json_file, "w")
json_str = json.dumps(json_dict)
json_fp.write(json_str)
json_fp.close()
xml_val_files = glob.glob(os.path.join(xml_val, "*.xml"))
xml_test_files = glob.glob(os.path.join(xml_test, "*.xml"))
xml_train_files = glob.glob(os.path.join(xml_train, "*.xml"))
convert(xml_val_files, coco_json_annotations + 'val2017.json')
convert(xml_test_files, coco_json_annotations+'test2017.json')
convert(xml_train_files, coco_json_annotations + 'train2017.json')
Fork PPYOLOE项目并启动运行
PPYOLOE目标检测训练框架
https://aistudio.baidu.com/aistudio/projectdetail/5756078
![](https://img-blog.csdnimg.cn/img_convert/af760cc396d74637bd5d8d2b6335740d.png)
按照main.ipynb流程依次执行即可!
导入所需要的第三方库
安装paddlex
创建数据集目录 将标注的图像数据上传到 MyDataset/JPEGImages 目录下;将coco格式数据标签annotations.json放到MyDataset目录下。
![](https://img-blog.csdnimg.cn/img_convert/83502f153e594bc18f559c9fd8efad17.png)
按比例切分数据集
git PaddleDetection代码
进入PaddleDetection目录
根据需求修改配置文件,比如检测的目标类别数 进入/home/aistudio/config_file/目录下,修改visdrone_detection.yml中num_classes参数
![](https://img-blog.csdnimg.cn/img_convert/31fc2d10663b4730b44b0a1fc839dedb.png)
开始训练
训练完成后评估模型
挑一张验证集的图片展示预测效果(可以到生成的目录下,打开查看)
导出模型,即可使用FastDeploy进行快速推理