visdrone是一个无人机的目标检测数据集，在很多目标检测的论文中都能看到它的身影。
标签从0到11分别为’ignored regions’,‘pedestrian’,‘people’,‘bicycle’,‘car’,‘van’,
‘truck’,‘tricycle’,‘awning-tricycle’,‘bus’,‘motor’,‘others’
现在先要用mmdetection自己训练一下这个数据集，需要把他转化为coco数据集格式
分两步走：
需要改的地方有注释，就是几个路径改一下即可
import os
from PIL import Image
# 把下面的root_dir路径改成你自己的路径即可
root_dir = r"D:\object_detection_data\datacovert\VisDrone2019-DET-val/"
annotations_dir = root_dir+"annotations/"
image_dir = root_dir + "images/"
xml_dir = root_dir+"Annotations_XML/" #在工作目录下创建Annotations_XML文件夹保存xml文件
# 下面的类别也换成你自己数据类别，也可适用于其他的数据集转换
class_name = ['ignored regions','pedestrian','people','bicycle','car','van',
'truck','tricycle','awning-tricycle','bus','motor','others']
for filename in os.listdir(annotations_dir):
fin = open(annotations_dir+filename, 'r')
image_name = filename.split('.')[0]
img = Image.open(image_dir+image_name+".jpg") # 若图像数据是“png”转换成“.png”即可
xml_name = xml_dir+image_name+'.xml'
with open(xml_name, 'w') as fout:
fout.write('<annotation>'+'\n')
fout.write('\t'+'<folder>VOC2007</folder>'+'\n')
fout.write('\t'+'<filename>'+image_name+'.jpg'+'</filename>'+'\n')
fout.write('\t'+'<source>'+'\n')
fout.write('\t\t'+'<database>'+'VisDrone2019-DET'+'</database>'+'\n')
fout.write('\t\t'+'<annotation>'+'VisDrone2019-DET'+'</annotation>'+'\n')
fout.write('\t\t'+'<image>'+'flickr'+'</image>'+'\n')
fout.write('\t\t'+'<flickrid>'+'Unspecified'+'</flickrid>'+'\n')
fout.write('\t'+'</source>'+'\n')
fout.write('\t'+'<owner>'+'\n')
fout.write('\t\t'+'<flickrid>'+'LJ'+'</flickrid>'+'\n')
fout.write('\t\t'+'<name>'+'LJ'+'</name>'+'\n')
fout.write('\t'+'</owner>'+'\n')
fout.write('\t'+'<size>'+'\n')
fout.write('\t\t'+'<width>'+str(img.size[0])+'</width>'+'\n')
fout.write('\t\t'+'<height>'+str(img.size[1])+'</height>'+'\n')
fout.write('\t\t'+'<depth>'+'3'+'</depth>'+'\n')
fout.write('\t'+'</size>'+'\n')
fout.write('\t'+'<segmented>'+'0'+'</segmented>'+'\n')
for line in fin.readlines():
line = line.split(',')
fout.write('\t'+'<object>'+'\n')
fout.write('\t\t'+'<name>'+class_name[int(line[5])]+'</name>'+'\n')
fout.write('\t\t'+'<pose>'+'Unspecified'+'</pose>'+'\n')
fout.write('\t\t'+'<truncated>'+line[6]+'</truncated>'+'\n')
fout.write('\t\t'+'<difficult>'+str(int(line[7]))+'</difficult>'+'\n')
fout.write('\t\t'+'<bndbox>'+'\n')
fout.write('\t\t\t'+'<xmin>'+line[0]+'</xmin>'+'\n')
fout.write('\t\t\t'+'<ymin>'+line[1]+'</ymin>'+'\n')
# pay attention to this point!(0-based)
fout.write('\t\t\t'+'<xmax>'+str(int(line[0])+int(line[2])-1)+'</xmax>'+'\n')
fout.write('\t\t\t'+'<ymax>'+str(int(line[1])+int(line[3])-1)+'</ymax>'+'\n')
fout.write('\t\t'+'</bndbox>'+'\n')
fout.write('\t'+'</object>'+'\n')
fin.close()
fout.write('</annotation>')
#!/usr/bin/python
# xml是voc的格式
# json是coco的格式
import sys, os, json, glob
import xml.etree.ElementTree as ET
INITIAL_BBOXIds = 1
# PREDEF_CLASSE = {}
PREDEF_CLASSE = { 'pedestrian': 1, 'people': 2,
'bicycle': 3, 'car': 4, 'van': 5, 'truck': 6, 'tricycle': 7,
'awning-tricycle': 8, 'bus': 9, 'motor': 10}
#我这里只想检测这十个类， 0和11没有加入转化。
# function
def get(root, name):
return root.findall(name)
def get_and_check(root, name, length):
vars = root.findall(name)
if len(vars) == 0:
raise NotImplementedError('Can not find %s in %s.'%(name, root.tag))
if length > 0 and len(vars) != length:
raise NotImplementedError('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 convert(xml_paths, out_json):
json_dict = {'images': [], 'type': 'instances',
'categories': [], 'annotations': []}
categories = PREDEF_CLASSE
bbox_id = INITIAL_BBOXIds
for image_id, xml_f in enumerate(xml_paths):
# 进度输出
sys.stdout.write('\r>> Converting image %d/%d' % (
image_id + 1, len(xml_paths)))
sys.stdout.flush()
tree = ET.parse(xml_f)
root = tree.getroot()
filename = get_and_check(root, 'filename', 1).text
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 + 1}
json_dict['images'].append(image)
## Cruuently 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 = max(categories.values()) + 1
categories[category] = new_id
category_id = categories[category]
bbox = get_and_check(obj, 'bndbox', 1)
xmin = int(get_and_check(bbox, 'xmin', 1).text) - 1
ymin = int(get_and_check(bbox, 'ymin', 1).text) - 1
xmax = int(get_and_check(bbox, 'xmax', 1).text)
ymax = int(get_and_check(bbox, 'ymax', 1).text)
if xmax <= xmin or ymax <= ymin:
continue
o_width = abs(xmax - xmin)
o_height = abs(ymax - ymin)
ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': image_id + 1,
'bbox': [xmin, ymin, o_width, o_height], 'category_id': category_id,
'id': bbox_id, 'ignore': 0, 'segmentation': []}
json_dict['annotations'].append(ann)
bbox_id = bbox_id + 1
for cate, cid in categories.items():
cat = {'supercategory': 'none', 'id': cid, 'name': cate}
json_dict['categories'].append(cat)
# json_file = open(out_json, 'w')
# json_str = json.dumps(json_dict)
# json_file.write(json_str)
# json_file.close() # 快
json.dump(json_dict, open(out_json, 'w'), indent=4) # indent=4 更加美观显示 慢
if __name__ == '__main__':
xml_path = r'D:\object_detection_data\datacovert\VisDrone2019-DET-val/Annotations_XML/' #改一下读取xml文件位置
xml_file = glob.glob(os.path.join(xml_path, '*.xml'))
convert(xml_file, r'D:\object_detection_data\datacovert\VisDrone2019-DET-val/NEW_val.json') #这里是生成的json保存位置，改一下
如图：
这里选用的是configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py模型。
首先下载对应权重，修改权重后面的全连接层的神经元个数
两阶段通用脚本如下，修改载入的权重和保存的权重名字运行即可。
import torch
pretrained_weights = torch.load('checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth')
num_class = 10
pretrained_weights['state_dict']['roi_head.bbox_head.fc_cls.weight'].resize_(num_class+1, 1024)
pretrained_weights['state_dict']['roi_head.bbox_head.fc_cls.bias'].resize_(num_class+1)
pretrained_weights['state_dict']['roi_head.bbox_head.fc_reg.weight'].resize_(num_class*4, 1024)
pretrained_weights['state_dict']['roi_head.bbox_head.fc_reg.bias'].resize_(num_class*4)
torch.save(pretrained_weights, "faster_rcnn_r50_fpn_1x_%d.pth"%num_class)
后面加载这个修改后的权重即可。
这里我只检测十个类 ，0 和11 对应的类没有检测。
接下来需要修改和类别相关的三个地方
修改完类别之后可以运行 下面这个命令检查标签对着没，对着就可以开始训练了。
python tools/misc/browse_dataset.py config/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py
这个数据集场景比较复杂，小物体的map非常低，还把人分成了pedestrain和people，个人感觉这俩太像了，容易误检，所以这俩的map贼低，感觉分类略多。
我训练输入图片大小1080x720 ,训练了十轮结果如下.map感人。
下面随便放了几张测试出来的结果。
上面这一张图我红色框内的物体明显就没检测到，可以看出小目标效果不得行。
就这样了吧
为了方便同行快速上手测试这个数据集，我把转好的json资源放在了百度云—链接随后放上来。
链接：https://pan.baidu.com/s/1BnpYSsViBnuT7FJq-nzxWw
提取码：1111