1.python提取COCO数据集中特定的类
安装pycocotools github地址：https://github.com/philferriere/cocoapi
pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
若报错，pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
换成
pip install git+git://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
实在不行的话，手动下载
python
git clone https://github.com/pdollar/coco.git
cd coco/PythonAPI
python setup.py build_ext --inplace #安装到本地
python setup.py build_ext install # 安装到Python环境中没有的库自己pip
注意skimage用pip install scikit-image -i https://pypi.tuna.tsinghua.edu.cn/simple
提取特定的类别如下：
python
# conding='utf-8'
from pycocotools.coco import COCO
import os
import shutil
from tqdm import tqdm
import skimage.io as io
import matplotlib.pyplot as plt
import cv2
from PIL import Image, ImageDraw
#the path you want to save your results for coco to voc
savepath="/opt/10T/home/asc005/YangMingxiang/DenseCLIP_/data/COCO/" #save_path
img_dir=savepath+'images/'
anno_dir=savepath+'Annotations/'
# datasets_list=['train2014', 'val2014']
datasets_list=['train2017', 'val2017']
classes_names = ['sheep'] #coco
#Store annotations and train2014/val2014/... in this folder
dataDir= '/opt/10T/home/asc005/YangMingxiang/DenseCLIP_/data/coco/' #origin coco
headstr = """\
<annotation>
<folder>VOC</folder>
<filename>%s</filename>
<source>
<database>My Database</database>
<annotation>COCO</annotation>
<image>flickr</image>
<flickrid>NULL</flickrid>
</source>
<owner>
<flickrid>NULL</flickrid>
<name>company</name>
</owner>
<size>
<width>%d</width>
<height>%d</height>
<depth>%d</depth>
</size>
<segmented>0</segmented>
"""
objstr = """\
<object>
<name>%s</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>%d</xmin>
<ymin>%d</ymin>
<xmax>%d</xmax>
<ymax>%d</ymax>
</bndbox>
</object>
"""
tailstr = '''\
</annotation>
'''
#if the dir is not exists,make it,else delete it
def mkr(path):
if os.path.exists(path):
shutil.rmtree(path)
os.mkdir(path)
else:
os.mkdir(path)
mkr(img_dir)
mkr(anno_dir)
def id2name(coco):
classes=dict()
for cls in coco.dataset['categories']:
classes[cls['id']]=cls['name']
return classes
def write_xml(anno_path,head, objs, tail):
f = open(anno_path, "w")
f.write(head)
for obj in objs:
f.write(objstr%(obj[0],obj[1],obj[2],obj[3],obj[4]))
f.write(tail)
def save_annotations_and_imgs(coco,dataset,filename,objs):
#eg:COCO_train2014_000000196610.jpg-->COCO_train2014_000000196610.xml
anno_path=anno_dir+filename[:-3]+'xml'
img_path=dataDir+dataset+'/'+filename
print(img_path)
dst_imgpath=img_dir+filename
img=cv2.imread(img_path)
#if (img.shape[2] == 1):
# print(filename + " not a RGB image")
# return
shutil.copy(img_path, dst_imgpath)
head=headstr % (filename, img.shape[1], img.shape[0], img.shape[2])
tail = tailstr
write_xml(anno_path,head, objs, tail)
def showimg(coco,dataset,img,classes,cls_id,show=True):
global dataDir
I=Image.open('%s/%s/%s'%(dataDir,dataset,img['file_name']))
annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)
# print(annIds)
anns = coco.loadAnns(annIds)
# print(anns)
# coco.showAnns(anns)
objs = []
for ann in anns:
class_name=classes[ann['category_id']]
if class_name in classes_names:
print(class_name)
if 'bbox' in ann:
bbox=ann['bbox']
xmin = int(bbox[0])
ymin = int(bbox[1])
xmax = int(bbox[2] + bbox[0])
ymax = int(bbox[3] + bbox[1])
obj = [class_name, xmin, ymin, xmax, ymax]
objs.append(obj)
draw = ImageDraw.Draw(I)
draw.rectangle([xmin, ymin, xmax, ymax])
if show:
plt.figure()
plt.axis('off')
plt.imshow(I)
plt.show()
return objs
for dataset in datasets_list:
#./COCO/annotations/instances_train2014.json
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataset)
#COCO API for initializing annotated data
coco = COCO(annFile)
#show all classes in coco
classes = id2name(coco)
print(classes)
#[1, 2, 3, 4, 6, 8]
classes_ids = coco.getCatIds(catNms=classes_names)
print(classes_ids)
for cls in classes_names:
#Get ID number of this class
cls_id=coco.getCatIds(catNms=[cls])
img_ids=coco.getImgIds(catIds=cls_id)
print(cls,len(img_ids))
# imgIds=img_ids[0:10]
for imgId in tqdm(img_ids):
img = coco.loadImgs(imgId)[0]
filename = img['file_name']
# print(filename)
objs=showimg(coco, dataset, img, classes,classes_ids,show=False)
print(objs)
save_annotations_and_imgs(coco, dataset, filename, objs)然后就可以了
2. 将上面获取的数据集划分为训练集和测试集
python
#conding='utf-8'
import os
import random
from shutil import copy2
# origin
image_original_path = "/opt/10T/home/asc005/YangMingxiang/DenseCLIP_/data/COCO/images"
label_original_path = "/opt/10T/home/asc005/YangMingxiang/DenseCLIP_/data/COCO/Annotations"
# parent_path = os.path.dirname(os.getcwd())
# parent_path = "D:\\AI_Find"
# train_image_path = os.path.join(parent_path, "image_data/seed/train/images/")
# train_label_path = os.path.join(parent_path, "image_data/seed/train/labels/")
train_image_path = os.path.join("/opt/10T/home/asc005/YangMingxiang/DenseCLIP_/data/COCO/train2017")
train_label_path = os.path.join("/opt/10T/home/asc005/YangMingxiang/DenseCLIP_/data/COCO/annotations/train2017")
test_image_path = os.path.join("/opt/10T/home/asc005/YangMingxiang/DenseCLIP_/data/COCO/val2017")
test_label_path = os.path.join("/opt/10T/home/asc005/YangMingxiang/DenseCLIP_/data/COCO/annotations/val2017")
# test_image_path = os.path.join(parent_path, 'image_data/seed/val/images/')
# test_label_path = os.path.join(parent_path, 'image_data/seed/val/labels/')
def mkdir():
if not os.path.exists(train_image_path):
os.makedirs(train_image_path)
if not os.path.exists(train_label_path):
os.makedirs(train_label_path)
if not os.path.exists(test_image_path):
os.makedirs(test_image_path)
if not os.path.exists(test_label_path):
os.makedirs(test_label_path)
def main():
mkdir()
all_image = os.listdir(image_original_path)
for i in range(len(all_image)):
num = random.randint(1,5)
if num != 2:
copy2(os.path.join(image_original_path, all_image[i]), train_image_path)
train_index.append(i)
else:
copy2(os.path.join(image_original_path, all_image[i]), test_image_path)
val_index.append(i)
all_label = os.listdir(label_original_path)
for i in train_index:
copy2(os.path.join(label_original_path, all_label[i]), train_label_path)
for i in val_index:
copy2(os.path.join(label_original_path, all_label[i]), test_label_path)
if __name__ == '__main__':
train_index = []
val_index = []
main()3.将上一步提取的COCO 某一类 xml转为COCO标准的json文件：
python
# -*- coding: utf-8 -*-
# @Time : 2019/8/27 10：48
# @Author :Rock
# @File : voc2coco.py
# just for object detection
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 = 0
image_id = 0
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.')
img_id = "%04d" % image_id
image_id += 1
image_item = dict()
image_item['id'] = int(img_id)
# 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):
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)
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 <folder>, <filename>, <size>, <object>
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 <size> 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 <width>, <height>, <depth>, <name>, <bndbox>
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 <xmin>, <ymin>, <xmax>, <ymax>, when subelem is <bndbox>
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 <object> 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__':
#修改这里的两个地址，一个是xml文件的父目录；一个是生成的json文件的绝对路径
xml_path = r'G:\dataset\COCO\person\coco_val2014\annotations\\'
json_file = r'G:\dataset\COCO\person\coco_val2014\instances_val2014.json'
parseXmlFiles(xml_path)
json.dump(coco, open(json_file, 'w'))