提取coco数据集的数据,##有个缺陷就是当多次提取不同类别的时候，train，val的json文件。类别id可能会不同，请自行手动修改.
提取特定的类：
# -*-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
# 需要设置的路径
savepath = "F:/coco-lei-fenbu/coco-donut/" # "F:/coco-fan/"
img_dir = savepath + 'images/'
anno_dir = savepath + 'annotations/'
datasets_list = ['train2017']
# coco有80类，这里写要提取类的名字，以person为例
classes_names = ['donut']
# 包含所有类别的原coco数据集路径
'''
目录格式如下：
$COCO_PATH
----|annotations
----|train2017
----|val2017
----|test2017
'''
dataDir = 'F:/coco/'
headstr = """\
VOC
%s
My Database
COCO
flickr
NULL
NULL
company
%d
%d
%d
"""
objstr = """\
%s
%s
%s
%s
%s
%s
"""
tailstr = '''\
'''
# 检查目录是否存在，如果存在，先删除再创建，否则，直接创建
def mkr(path):
if not os.path.exists(path):
os.makedirs(path) # 可以创建多级目录
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], obj[5], obj[6]))
f.write(tail)
def save_annotations_and_imgs(coco, dataset, filename, objs):
# 将图片转为xml，例:COCO_train2017_000000196610.jpg-->COCO_train2017_000000196610.xml
dst_anno_dir = os.path.join(anno_dir, dataset)
mkr(dst_anno_dir)
anno_path = dst_anno_dir + '/' + filename[:-3] + 'xml'
img_path = dataDir + dataset + '/' + filename
print("img_path: ", img_path)
dst_img_dir = os.path.join(img_dir, dataset)
mkr(dst_img_dir)
dst_imgpath = dst_img_dir + '/' + filename
print("dst_imgpath: ", dst_imgpath)
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']))
# 通过id，得到注释的信息
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 'segmentation' in ann:
segmentation = ann["segmentation"]
if 'area' in ann:
area = ann["area"]
if 'bbox' in ann:
bbox = ann['bbox']
xmin = float(bbox[0])
ymin = float(bbox[1])
xmax = float(bbox[2]) # bbox[2] + bbox[0]
ymax = float(bbox[3]) # bbox[3] + bbox[1]
obj = [class_name, segmentation, area, 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_train2017.json
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataset)
# 使用COCO API用来初始化注释数据
coco = COCO(annFile)
# 获取COCO数据集中的所有类别
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:
# 获取该类的id
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)xml转json
# -*-coding:utf-8-*-
# -*-coding:utf-8-*-
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 = 20210000000
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, segmentation, area):
global annotation_id
annotation_item = dict()
annotation_item['segmentation'] = segmentation
annotation_item['area'] = area
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()
segmentation = []
area = []
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 , , ,
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['cx'] = None
bndbox['cy'] = None
bndbox['w'] = None
bndbox['h'] = 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)
elif current_parent == 'object' and subelem.tag == 'segmentation':
segmentation = eval(subelem.text)
elif current_parent == 'object' and subelem.tag == 'area':
area = float(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] = float(option.text)
# only after parse the tag
if bndbox['cx'] is not None:#'xmin'
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['cx'])
# y
bbox.append(bndbox['cy'])
# w
bbox.append(bndbox['w'])
# h
bbox.append(bndbox['h'])
print('add annotation with {},{},{},{},{},{}'.format(object_name, current_image_id, current_category_id,
bbox,segmentation,area))
addAnnoItem(object_name, current_image_id, current_category_id, bbox, segmentation, area)
if __name__ == '__main__':
# 需要自己设定的地址，一个是已生成的是xml文件的父目录；一个是要生成的json文件的目录
xml_dir = r'F:\coco-lei-fenbu\coco-donut\annotations'
json_dir = r'F:\coco-lei-fenbu\coco-donut\annotations'
dataset_lists = ['val2017']
for dataset in dataset_lists:
xml_path = os.path.join(xml_dir, dataset)
json_file = json_dir + '/{}.json'.format(dataset)
parseXmlFiles(xml_path)
json.dump(coco, open(json_file, 'w'))
