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coco2voc.py
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'''
安装依赖：
!git clone https://gitee.com/yanxin_thomas/cocoapi.git
%cd cocoapi/PythonAPI/
!make
!python setup.py install
!pip install lxml
'''
'''
修改部分
# 转换后的VOC存放地址
CKimg_dir ='/home/aistudio/dataset/VOC/images'
CKanno_dir = '/home/aistudio/dataset/VOC/annotations'
def get_CK5(origin_anno_dir,origin_image_dir,verbose=False):
dataTypes = ['train2017'] #需要根据COCO数据集作具体修改
for dataType in dataTypes:
annFile = 'train.json'
annpath=os.path.join(origin_anno_dir,annFile)
coco = COCO(annpath)
classes = catid2name(coco)
imgIds = coco.getImgIds()
# imgIds=imgIds[0:1000]#测试用，抽取10张图片，看下存储效果
for imgId in tqdm(imgIds):
img = coco.loadImgs(imgId)[0]
showbycv(coco, dataType, img, classes,origin_image_dir,verbose=False)
def main():
base_dir='/home/aistudio/dataset/VOC'#step1 这里是一个新的文件夹，存放转换后的图片和标注
image_dir=os.path.join(base_dir,'JPEGImages')#在上述文件夹中生成JPEGImages，Annotations两个子文件夹
anno_dir=os.path.join(base_dir,'Annotations')
mkr(image_dir)
mkr(anno_dir)
origin_image_dir='/home/aistudio/dataset/train/images'#step 2原始的coco的图像存放位置
origin_anno_dir='/home/aistudio/dataset/train/annotations'#step 3 原始的coco的标注存放位置
verbose=True #是否需要看下标记是否正确的开关标记，若是true,就会把标记展示到图片上
get_CK5(origin_anno_dir,origin_image_dir,verbose)
'''
from pycocotools.coco import COCO #这个包可以从git上下载https://github.com/cocodataset/cocoapi/tree/master/PythonAPI，也可以直接用修改后的coco.py
import os, cv2, shutil
from lxml import etree, objectify
from tqdm import tqdm
from PIL import Image
# 转换后的VOC存放地址
CKimg_dir ='/home/aistudio/dataset/VOC/images'
CKanno_dir = '/home/aistudio/dataset/VOC/annotations'
#若模型保存文件夹不存在，创建模型保存文件夹，若存在，删除重建
def mkr(path):
if os.path.exists(path):
shutil.rmtree(path)
os.mkdir(path)
else:
os.mkdir(path)
def save_annotations(filename, objs,filepath):
annopath = CKanno_dir + "/" + filename[:-3] + "xml" #生成的xml文件保存路径
dst_path = CKimg_dir + "/" + filename
img_path=filepath
img = cv2.imread(img_path)
im = Image.open(img_path)
if im.mode != "RGB":
print(filename + " not a RGB image")
im.close()
return
im.close()
shutil.copy(img_path, dst_path)#把原始图像复制到目标文件夹
E = objectify.ElementMaker(annotate=False)
anno_tree = E.annotation(
E.folder('1'),
E.filename(filename),
E.source(
E.database('CKdemo'),
E.annotation('VOC'),
E.image('CK')
),
E.size(
E.width(img.shape[1]),
E.height(img.shape[0]),
E.depth(img.shape[2])
),
E.segmented(0)
)
for obj in objs:
E2 = objectify.ElementMaker(annotate=False)
anno_tree2 = E2.object(
E.name(obj[0]),
E.pose(),
E.truncated("0"),
E.difficult(0),
E.bndbox(
E.xmin(obj[2]),
E.ymin(obj[3]),
E.xmax(obj[4]),
E.ymax(obj[5])
)
)
anno_tree.append(anno_tree2)
etree.ElementTree(anno_tree).write(annopath, pretty_print=True)
def showbycv(coco, dataType, img, classes,origin_image_dir,verbose=False):
filename = img['file_name']
filepath=os.path.join(origin_image_dir,dataType,filename)
I = cv2.imread(filepath)
annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None)
anns = coco.loadAnns(annIds)
objs = []
for ann in anns:
name = classes[ann['category_id']]
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 = [name, 1.0, xmin, ymin, xmax, ymax]
objs.append(obj)
if verbose:
cv2.rectangle(I, (xmin, ymin), (xmax, ymax), (255, 0, 0))
cv2.putText(I, name, (xmin, ymin), 3, 1, (0, 0, 255))
save_annotations(filename, objs,filepath)
if verbose:
cv2.imshow("img", I)
cv2.waitKey(0)
def catid2name(coco):#将名字和id号建立一个字典
classes = dict()
for cat in coco.dataset['categories']:
classes[cat['id']] = cat['name']
# print(str(cat['id'])+":"+cat['name'])
return classes
def get_CK5(origin_anno_dir,origin_image_dir,verbose=False):
dataTypes = ['train2017'] #需要根据COCO数据集作具体修改
for dataType in dataTypes:
annFile = 'train.json'
annpath=os.path.join(origin_anno_dir,annFile)
coco = COCO(annpath)
classes = catid2name(coco)
imgIds = coco.getImgIds()
# imgIds=imgIds[0:1000]#测试用，抽取10张图片，看下存储效果
for imgId in tqdm(imgIds):
img = coco.loadImgs(imgId)[0]
showbycv(coco, dataType, img, classes,origin_image_dir,verbose=False)
def main():
base_dir='/home/aistudio/dataset/VOC'#step1 这里是一个新的文件夹，存放转换后的图片和标注
image_dir=os.path.join(base_dir,'JPEGImages')#在上述文件夹中生成JPEGImages，Annotations两个子文件夹
anno_dir=os.path.join(base_dir,'Annotations')
mkr(image_dir)
mkr(anno_dir)
origin_image_dir='/home/aistudio/dataset/train/images'#step 2原始的coco的图像存放位置
origin_anno_dir='/home/aistudio/dataset/train/annotations'#step 3 原始的coco的标注存放位置
verbose=True #是否需要看下标记是否正确的开关标记，若是true,就会把标记展示到图片上
get_CK5(origin_anno_dir,origin_image_dir,verbose)
if __name__ == "__main__":
main()