voc2coco
原文链接： voc2coco
<annotation>
<folder>VOC2012</folder>
<filename>2007_000392.jpg</filename> //文件名
<source> //图像来源（不重要）
<database>The VOC2007 Database</database>
<annotation>PASCAL VOC2007</annotation>
<image>flickr</image>
</source>
<size> //图像尺寸（长宽以及通道数）
<width>500</width>
<height>332</height>
<depth>3</depth>
</size>
<segmented>1</segmented> //是否用于分割（在图像物体识别中01无所谓）
<object> //检测到的物体
<name>horse</name> //物体类别
<pose>Right</pose> //拍摄角度
<truncated>0</truncated> // 目标是否被截断（0表示完整）
<difficult>0</difficult> //目标是否难以识别（0表示容易识别）
<bndbox> //bounding-box（包含左下角和右上角xy坐标）
<xmin>100</xmin>
<ymin>96</ymin>
<xmax>355</xmax>
<ymax>324</ymax>
</bndbox>
</object>
<object> //检测到多个物体
<name>person</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>198</xmin>
<ymin>58</ymin>
<xmax>286</xmax>
<ymax>197</ymax>
</bndbox>
</object>
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 08 16:01:57 2018
Convert VOC dataset into COCO dataset
@author: wkoa
"""
import os
import sys
import json
import xml.etree.ElementTree as ET
import numpy as np
import cv2
def _isArrayLike(obj):
return hasattr(obj, '__iter__') and hasattr(obj, '__len__')
class voc2coco:
def __init__(self, devkit_path = None, year = None):
self.classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
#self.classes = ('__background__',
# 'bottle','box','brush','cabbage','dolphin',
# 'eggplant','hedgehog','lion','polarbear',
# 'squirrel')
self.num_classes = len(self.classes)
assert 'VOCdevkit' in devkit_path, 'VOCdevkit path does not exist: {}'.format(devkit_path)
self.data_path = os.path.join(devkit_path,'VOC'+ year)
self.annotaions_path = os.path.join(self.data_path,'Annotations')
self.image_set_path = os.path.join(self.data_path,'ImageSets')
self.year = year
self.categories_to_ids_map = self._get_categories_to_ids_map()
self.categories_msg = self._categories_msg_generator()
def _load_annotation(self,ids=[]):
"""
Load annotations by ids
:param ids (int array) : get amms for idss
:return image_msg
return annotation_msg
"""
ids = ids if _isArrayLike(ids) else [ids]
image_msg = []
annotation_msg = []
annotation_id = 1
for index in ids:
filename = '{:0>6}'.format(index)
json_file = os.path.join(self.data_path,'Segmentation_json',filename+'.json')
#Labelme label file .json
if os.path.exists(json_file):
img_file = os.path.join(self.data_path,'JPEGImages',filename+'.jpg')
im = cv2.imread(img_file)
width = im.shape[1]
height = im.shape[0]
seg_data = json.load(open(json_file,'r'))
assert type(seg_data)==type(dict()), 'annotation file format {} not supported'.format(type(seg_data))
for shape in seg_data['shapes']:
seg_msg = []
for point in shape['points']:
seg_msg += point
one_ann_msg = { "segmentation":[seg_msg],
"area":self._area_computer(shape['points']),
"iscrowd":0,
"image_id":int(index),
"bbox":self._points_to_mbr(shape['points']),
"category_id":self.categories_to_ids_map[shape['label']],
"id":annotation_id,
"ignore":0
}
annotation_msg.append(one_ann_msg)
annotation_id += 1
#LabelImg label file .xml
else:
xml_file = os.path.join(self.annotaions_path,filename+'.xml')
tree = ET.parse(xml_file)
size = tree.find('size')
objs = tree.findall('object')
width = size.find('width').text
height = size.find('height').text
for obj in objs:
bndbox = obj.find('bndbox')
[xmin,xmax,ymin,ymax] \
= [int(bndbox.find('xmin').text)-1,int(bndbox.find('xmax').text),int(bndbox.find('ymin').text)-1,int(bndbox.find('ymax').text)]
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
bbox = [xmin,xmax,ymin,ymax]
one_ann_msg = { "segmentation":self._bbox_to_mask(bbox),
"area":self._bbox_area_computer(bbox),
"iscrowd":0,
"image_id":int(index),
"bbox":[xmin,ymin,xmax-xmin,ymax-ymin],
"category_id":self.categories_to_ids_map[obj.find('name').text],
"id":annotation_id,
"ignore":0
}
annotation_msg.append(one_ann_msg)
annotation_id += 1
one_image_msg = { "file_name":filename+".jpg",
"height":int(height),
"width":int(width),
"id":int(index)
}
image_msg.append(one_image_msg)
return image_msg, annotation_msg
def _bbox_to_mask(self,bbox):
""""
Generate mask by bbox
:param bbox e.g. [xmin,xmax,ymin,ymax]
:return mask [points]
"""
assert len(bbox) == 4, 'Wrong bndbox!'
mask = [bbox[0],bbox[2],bbox[0],bbox[3],bbox[1],bbox[3],bbox[1],bbox[2]]
return [mask]
def _bbox_area_computer(self,bbox):
"""
Area computer
"""
width = bbox[1]-bbox[0]
height = bbox[3]-bbox[2]
return width*height
def _save_json_file(self, filename = None, data = None):
"""
Save result in json
:param filename (str) : name of json file
param data : coco format data
:return
"""
json_path = os.path.join(self.data_path,'cocoformatJson')
assert filename is not None, 'lack filename'
if os.path.exists(json_path) == False:
os.mkdir(json_path)
if not filename.endswith('.json'):
filename += '.json'
assert type(data) == type(dict()), 'data format {} not supported'.format(type(data))
with open(os.path.join(json_path,filename),'w') as f:
f.write(json.dumps(data))
def _get_categories_to_ids_map(self):
"""
Generate categories to ids map
"""
return dict(zip(self.classes,xrange(self.num_classes)))
def _get_all_indexs(self):
"""
Get all images and annotations indexs
:param
:return ids (str array)
"""
ids = []
for root, dirs, files in os.walk(self.annotaions_path,topdown=False):
for f in files:
if str(f).endswith('.xml'):
id = int(str(f).strip('.xml'))
ids.append(id)
assert ids is not None, 'There is none xml file in {}'.format(self.annotaions_path)
return ids
def _get_indexs_by_image_set(self, image_set = None):
"""
Get images and nnotations indexs in image_set
"""
if image_set is None:
return self._get_all_indexs()
else:
image_set_path = os.path.join(self.image_set_path,'Main',image_set+'.txt')
assert os.path.exists(image_set_path), 'Path does not exist: {}'.format(image_set_path)
with open(image_set_path) as f:
ids = [x.strip() for x in f.readlines()]
return ids
def _points_to_mbr(self,points):
"""
Transfer points to min bounding rectangle
:param: points (a list of lists)
:return: [x,y,width,height]
"""
assert _isArrayLike(points), 'Points should be array like!'
x = [point[0] for point in points]
y = [point[1] for point in points]
assert len(x)==len(y),'Wrong point quantity'
xmin, xmax, ymin, ymax = min(x),max(x),min(y),max(y)
height = ymax - ymin
width = xmax - xmin
return [xmin,ymin,width,height]
def _categories_msg_generator(self):
categories_msg = []
for category in self.classes:
if category == '__background__':
continue
one_categories_msg = { "supercategory":"none",
"id":self.categories_to_ids_map[category],
"name":category
}
categories_msg.append(one_categories_msg)
return categories_msg
def _area_computer(self,points):
"""
:param: one shape's points (int array array)
:return: shape's area
"""
assert _isArrayLike(points), 'Points should be array like!'
tmp_contour = []
for point in points:
tmp_contour.append([point])
contour = np.array(tmp_contour,dtype=np.int32)
area = cv2.contourArea(contour)
return area
def voc_to_coco_converter(self):
"""
Convert voc dataset to coco dataset
"""
img_sets = ['trainval','test']
for img_set in img_sets:
ids = self._get_indexs_by_image_set(img_set)
img_msg, ann_msg = self._load_annotation(ids)
result_json = { "images":img_msg,
"type": "instances",
"annotations":ann_msg,
"categories":self.categories_msg}
self._save_json_file('voc_'+self.year+'_'+img_set,result_json)
def demo():
converter = voc2coco('D:\\MyCAS\\VOC2007\\VOCdevkit','2007')
converter.voc_to_coco_converter()
if __name__ == "__main__":
demo()