目标检测部分----baseline

数据预处理部分(附VOC,COCO数据集链接)

1.1赛题数据转VOC格式 and VOC标签转COCO标签

现在做人工智能比赛，一般都是需要预处理数据，并把数据转换成COCO格式或者VOC格式，接下来给大家分享如何将本赛题的检测数据转为VOC格式。

下图为此次竞赛给出的数据格式：

train.txt中的标签数据的具体说明如下：

如果检测目标有多个时，后面会用空格间隔，继续X1,Y1,X2,Y2 ,type，confidence这种格式。而type包含的类别编号对应的类别名称如下表所示，background 背景为0。

第一步：将train.txt按图像分成多个txt文件

Fen_hang.py:

f = open("train.txt","r")

lines = f.readlines()

for line in lines:

line

txt = line[0:6]

#print(txt)

f=txt+'.txt'

file = open(f, 'w')

#print(f)

line=line[22:]

#print(line)

for db in line.split():

​ #print(db)

​ file.write(db[:-2])

​ file.write('\n')

效果如下：

第二步：将txt文件转换为xmlVOC类标签文件

Txt_xml.py:

#! /usr/bin/python

# -- coding:UTF-8 --

import os, sys

import glob

from PIL import Image

# VEDAI 图像存储位置

src_img_dir = "/home/cb/code/zidonjiashi/dataset/test/"#图片存放地址

# VEDAI 图像的 ground truth 的 txt 文件存放位置

src_txt_dir = "/home/cb/code/zidonjiashi/txt_to_coco/weibiaoqian_test/"#txt存放地址

src_xml_dir = "/home/cb/code/zidonjiashi/txt_to_coco/anno1/"#生成xml存放地址

img_Lists = glob.glob(src_img_dir + '/*.jpg')

img_basenames = [] # e.g. 100.jpg

for item in img_Lists:

img_basenames.append(os.path.basename(item))

img_names = [] # e.g. 100

for item in img_basenames:

temp1, temp2 = os.path.splitext(item)

img_names.append(temp1)

for img in img_names:

im = Image.open((src_img_dir + '/' + img + '.jpg'))

width, height = im.size

# open the crospronding txt file

gt = open(src_txt_dir + '/' + img + '.txt').read().splitlines()

#gt = open(src_txt_dir + '/gt_' + img + '.txt').read().splitlines()

# write in xml file

#os.mknod(src_xml_dir + '/' + img + '.xml')

xml_file = open((src_xml_dir + '/' + img + '.xml'), 'w')

xml_file.write('\n')

xml_file.write(' VOC2007\n')

xml_file.write(' ' + str(img) + '.jpg' + '\n')

xml_file.write(' \n')

xml_file.write(' ' + str(width) + '\n')

xml_file.write(' ' + str(height) + '\n')

xml_file.write(' 3\n')

xml_file.write(' \n')

# write the region of image on xml file

for img_each_label in gt:

​ spt = img_each_label.split(',') #这里如果txt里面是以逗号‘，’隔开的，那么就改为spt = img_each_label.split(',')。

​ xml_file.write(' \n')

​ xml_file.write(' ' + str(spt[4]) + '\n')

​ xml_file.write(' Unspecified\n')

​ xml_file.write(' 0\n')

​ xml_file.write(' 0\n')

​ xml_file.write(' \n')

​ xml_file.write(' ' + str(spt[0]) + '\n')

​ xml_file.write(' ' + str(spt[1]) + '\n')

​ xml_file.write(' ' + str(spt[2]) + '\n')

​ xml_file.write(' ' + str(spt[3]) + '\n')

​ xml_file.write(' \n')

​ xml_file.write('

\n')

xml_file.write('')

至此，生成xml标签文件如下，再按如下顺序存放，便成了VOC数据集。

第三步：VOC标签转COCO标签

Voccoco.py:

import xml.etree.ElementTree as ET

import os

import json

coco = dict()

coco['images'] = []

coco['type'] = 'instances'

coco['annotations'] = []

coco['categories'] = []

category_set = dict()

image_set = set()

category_item_id = -1

image_id = 7000

annotation_id = 0

def addCatItem(name):

global category_item_id

category_item = dict()

category_item['supercategory'] = 'none'

category_item_id += 1

category_item['id'] = category_item_id

category_item['name'] = name

coco['categories'].append(category_item)

category_set[name] = category_item_id

return category_item_id

def addImgItem(file_name, size):

global image_id

if file_name is None:

​ raise Exception('Could not find filename tag in xml file.')

if size['width'] is None:

​ raise Exception('Could not find width tag in xml file.')

if size['height'] is None:

​ raise Exception('Could not find height tag in xml file.')

image_id += 1

image_item = dict()

image_item['id'] = image_id

image_item['file_name'] = file_name

image_item['width'] = size['width']

image_item['height'] = size['height']

coco['images'].append(image_item)

image_set.add(file_name)

return image_id

def addAnnoItem(object_name, image_id, category_id, bbox):

global annotation_id

annotation_item = dict()

annotation_item['segmentation'] = []

seg = []

# bbox[] is x,y,w,h

# left_top

seg.append(bbox[0])

seg.append(bbox[1])

# left_bottom

seg.append(bbox[0])

seg.append(bbox[1] + bbox[3])

# right_bottom

seg.append(bbox[0] + bbox[2])

seg.append(bbox[1] + bbox[3])

# right_top

seg.append(bbox[0] + bbox[2])

seg.append(bbox[1])

annotation_item['segmentation'].append(seg)

annotation_item['area'] = bbox[2] * bbox[3]

annotation_item['iscrowd'] = 0

annotation_item['ignore'] = 0

annotation_item['image_id'] = image_id

annotation_item['bbox'] = bbox

annotation_item['category_id'] = category_id

annotation_id += 1

annotation_item['id'] = annotation_id

coco['annotations'].append(annotation_item)

def parseXmlFiles(xml_path):

n=0

for f in os.listdir(xml_path):

​ if not f.endswith('.xml'):

​ continue

​ bndbox = dict()

​ size = dict()

​ current_image_id = None

​ current_category_id = None

​ file_name = None

​ size['width'] = None

​ size['height'] = None

​ size['depth'] = None

​ xml_file = os.path.join(xml_path, f)

​ print(xml_file)

​ print(n)

​ n=n+1;

​ tree = ET.parse(xml_file)

​ root = tree.getroot()

​ if root.tag != 'annotation':

​ raise Exception('pascal voc xml root element should be annotation, rather than {}'.format(root.tag))

​ # elem is , , ,

​ for elem in root:

​ current_parent = elem.tag

​ current_sub = None

​ object_name = None

​ if elem.tag == 'folder':

​ continue

​ if elem.tag == 'filename':

​ file_name = elem.text

​ if file_name in category_set:

​ raise Exception('file_name duplicated')

​ # add img item only after parse tag

​ elif current_image_id is None and file_name is not None and size['width'] is not None:

​ if file_name not in image_set:

​ current_image_id = addImgItem(file_name, size)

​ print('add image with {} and {}'.format(file_name, size))

​ else:

​ raise Exception('duplicated image: {}'.format(file_name))

​ # subelem is , , , ,

​ for subelem in elem:

​ bndbox['xmin'] = None

​ bndbox['xmax'] = None

​ bndbox['ymin'] = None

​ bndbox['ymax'] = None

​ current_sub = subelem.tag

​ if current_parent == 'object' and subelem.tag == 'name':

​ object_name = subelem.text

​ if object_name not in category_set:

​ current_category_id = addCatItem(object_name)

​ else:

​ current_category_id = category_set[object_name]

​ elif current_parent == 'size':

​ if size[subelem.tag] is not None:

​ raise Exception('xml structure broken at size tag.')

​ size[subelem.tag] = int(subelem.text)

​ # option is , , , , when subelem is

​ for option in subelem:

​ if current_sub == 'bndbox':

​ if bndbox[option.tag] is not None:

​ raise Exception('xml structure corrupted at bndbox tag.')

​ bndbox[option.tag] = int(option.text)

​ # only after parse the tag

​ if bndbox['xmin'] is not None:

​ if object_name is None:

​ raise Exception('xml structure broken at bndbox tag')

​ if current_image_id is None:

​ raise Exception('xml structure broken at bndbox tag')

​ if current_category_id is None:

​ raise Exception('xml structure broken at bndbox tag')

​ bbox = []

​ # x

​ bbox.append(bndbox['xmin'])

​ # y

​ bbox.append(bndbox['ymin'])

​ # w

​ bbox.append(bndbox['xmax'] - bndbox['xmin'])

​ # h

​ bbox.append(bndbox['ymax'] - bndbox['ymin'])

​ print('add annotation with {},{},{},{}'.format(object_name, current_image_id, current_category_id,

​ bbox))

​ addAnnoItem(object_name, current_image_id, current_category_id, bbox)

if name == 'main':

xml_path = '/home/cb/code/zidonjiashi/txt_to_coco/anno1/' # 这是xml文件所在的地址

json_file = '/home/cb/code/zidonjiashi/xml_to_json/wei_test/test.json' # 这是你要生成的json文件

parseXmlFiles(xml_path) # 只需要改动这两个参数就行了

json.dump(coco, open(json_file, 'w'))

VOC,COCO数据集百度云链接：

链接：https://pan.baidu.com/s/1uwp_xlJNi1dXQ8SZbTEfig

提取码：i6da