以下代码是将coco2017数据集标签格式转到voc2007格式的代码.
# -*- coding: 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
savepath = "coco2017toVOC2007/"
datasets_list = ['train2017'] ##运行完之后再改为val2017再运行一次
img_dir = savepath + 'images_train/' #####这个路径会把你处理的图片拷贝进来，这里只处理了train2017文件夹下的数据，所以处理好之后需要修改生成image文件夹的名称为train2017
anno_dir = savepath + 'annotations_train/' # 当前目录下会生成annotations文件夹存放xml，结束后修改名称
# classes_names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
# 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
# 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase',
# 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
# 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
# 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
# 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
# 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
# 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
classes_names = ['traffic light']
dataDir = 'COCO2017' ####### 连接到coco的数据集
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>
'''
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):
anno_path = anno_dir + filename[:-3] + 'xml'
print('anno_path:%s' % anno_path)
# img_path=dataDir+'/'+'images'+'/'+dataset+'/'+filename
img_path = dataDir + '/' + dataset + '/' + filename
print('img_path:%s' % img_path)
print('step3-image-path-OK')
dst_imgpath = img_dir + filename
img = cv2.imread(img_path)
'''if (img.shape[2] == 1):
print(filename + " not a RGB image")
return'''
print('img_path:%s' % img_path)
print('dst_imgpath:%s' % dst_imgpath)
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/%s'%(dataDir,'images',dataset,img['file_name']))
I = Image.open('%s/%s/%s' % (dataDir, dataset, img['file_name'])) ########may be you can changed
annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)
anns = coco.loadAnns(annIds)
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:
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataset) # 存放json文件的路径
print('annFile:%s' % annFile)
coco = COCO(annFile)
'''
loading annotations into memory...
Done (t=0.81s)
creating index...
index created!
'''
classes = id2name(coco)
print("classes:%s" % classes)
classes_ids = coco.getCatIds(catNms=classes_names)
print(classes_ids)
for cls in classes_names:
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)
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