1. 实现起来比较简单，直接看代码
import os
from tqdm import tqdm
import xml.etree.ElementTree as ET
import json
class_names = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse','motorbike',
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
def voc2coco(data_dir, train_file, test_file):
xml_dir = os.path.join(data_dir, 'Annotations')
img_dir = os.path.join(data_dir, 'JPEGImages')
with open(train_file, 'r') as f:
train_fs = f.readlines()
train_xmls = [os.path.join(xml_dir, n.strip() + '.xml') for n in train_fs]
with open(test_file, 'r') as f:
test_fs = f.readlines()
test_xmls = [os.path.join(xml_dir, n.strip() + '.xml') for n in test_fs]
print('got xmls')
train_coco = xml2coco(train_xmls)
test_coco = xml2coco(test_xmls)
with open(os.path.join(data_dir, 'coco_train.json'), 'w') as f:
json.dump(train_coco, f, ensure_ascii=False, indent=2)
with open(os.path.join(data_dir, 'coco_test.json'), 'w') as f:
json.dump(test_coco, f, ensure_ascii=False, indent=2)
print('done')
def xml2coco(xmls):
coco_anno = {'info': {}, 'images': [], 'licenses': [], 'annotations': [], 'categories': []}
coco_anno['categories'] = [{'supercategory': j, 'id': i+1, 'name': j} for i,j in enumerate(class_names)]
img_id = 0
anno_id = 0
for fxml in tqdm(xmls):
try:
tree = ET.parse(fxml)
objects = tree.findall('object')
except:
print('err xml file: ', fxml)
continue
if len(objects) < 1:
print('no object in ', fxml)
continue
img_id += 1
size = tree.find('size')
ih = float(size.find('height').text)
iw = float(size.find('width').text)
img_name = fxml.strip().split('/')[-1].replace('xml', 'jpg')
img_info = {}
img_info['id'] = img_id
img_info['file_name'] = img_name
img_info['height'] = ih
img_info['width'] = iw
coco_anno['images'].append(img_info)
for obj in objects:
cls_name = obj.find('name').text
bbox = obj.find('bndbox')
x1 = float(bbox.find('xmin').text)
y1 = float(bbox.find('ymin').text)
x2 = float(bbox.find('xmax').text)
y2 = float(bbox.find('ymax').text)
if x2 < x1 or y2 < y1:
print('bbox not valid: ', fxml)
continue
anno_id += 1
bb = [x1, y1, x2 - x1, y2 - y1]
categery_id = class_names.index(cls_name) + 1
area = (x2 - x1) * (y2 - y1)
anno_info = {}
anno_info['segmentation'] = []
anno_info['area'] = area
anno_info['image_id'] = img_id
anno_info['bbox'] = bb
anno_info['iscrowd'] = 0
anno_info['category_id'] = categery_id
anno_info['id'] = anno_id
coco_anno['annotations'].append(anno_info)
return coco_anno
if __name__ == '__main__':
data_dir = '/home/gp/work/project/learning/VOC/VOCdevkit/VOC2007'
train_file = '/home/gp/work/project/learning/VOC/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt'
test_file = '/home/gp/work/project/learning/VOC/VOCdevkit/VOC2007/ImageSets/Main/test.txt'
voc2coco(data_dir, train_file, test_file)
2. 转化之后的结果
Caption
