我们一般使用的数据格式是voc2007的数据格式，有时我们也需要用coco数据格式，这种格式一般以json格式存储，那么如将voc2007格式的数据转成coco数据格式呢？下面是python的实现：
# -*- coding:utf-8 -*-
# !/usr/bin/env python
import argparse
import json
import matplotlib.pyplot as plt
import skimage.io as io
import cv2
from labelme import utils
import numpy as np
import glob
import PIL.Image
import os,sys
class PascalVOC2coco(object):
def __init__(self, xml=[], save_json_path='./new.json'):
'''
:param xml: 所有Pascal VOC的xml文件路径组成的列表
:param save_json_path: json保存位置
'''
self.xml = xml
self.save_json_path = save_json_path
self.images = []
self.categories = []
self.annotations = []
# self.data_coco = {}
self.label = []
self.annID = 1
self.height = 0
self.width = 0
self.ob = []
self.save_json()
def data_transfer(self):
for num, json_file in enumerate(self.xml):
# 进度输出
sys.stdout.write('\r>> Converting image %d/%d' % (
num + 1, len(self.xml)))
sys.stdout.flush()
self.json_file = json_file
#print("self.json", self.json_file)
self.num = num
#print(self.num)
path = os.path.dirname(self.json_file)
#print(path)
path = os.path.dirname(path)
#print(path)
# path=os.path.split(self.json_file)[0]
# path=os.path.split(path)[0]
obj_path = glob.glob(os.path.join(path, 'SegmentationObject', '*.png'))
#print(obj_path)
with open(json_file, 'r') as fp:
#print(fp)
flag = 0
for p in fp:
#print(p)
# if 'folder' in p:
# folder =p.split('>')[1].split('<')[0]
f_name = 1
if 'filename' in p:
self.filen_ame = p.split('>')[1].split('<')[0]
#print(self.filen_ame)
f_name = 0
self.path = os.path.join(path, 'SegmentationObject', self.filen_ame.split('.')[0] + '.png')
#if self.path not in obj_path:
# break
if 'width' in p:
self.width = int(p.split('>')[1].split('<')[0])
#print(self.width)
if 'height' in p:
self.height = int(p.split('>')[1].split('<')[0])
self.images.append(self.image())
#print(self.image())
if flag == 1:
self.supercategory = self.ob[0]
if self.supercategory not in self.label:
self.categories.append(self.categorie())
self.label.append(self.supercategory)
# 边界框
x1 = int(self.ob[1]);
y1 = int(self.ob[2]);
x2 = int(self.ob[3]);
y2 = int(self.ob[4])
self.rectangle = [x1, y1, x2, y2]
self.bbox = [x1, y1, x2 - x1, y2 - y1] # COCO 对应格式[x,y,w,h]
self.annotations.append(self.annotation())
self.annID += 1
self.ob = []
flag = 0
elif f_name == 1:
if 'name' in p:
self.ob.append(p.split('>')[1].split('<')[0])
if 'xmin' in p:
self.ob.append(p.split('>')[1].split('<')[0])
if 'ymin' in p:
self.ob.append(p.split('>')[1].split('<')[0])
if 'xmax' in p:
self.ob.append(p.split('>')[1].split('<')[0])
if 'ymax' in p:
self.ob.append(p.split('>')[1].split('<')[0])
flag = 1
'''
if '<object>' in p:
# 类别
print(next(fp))
d = [next(fp).split('>')[1].split('<')[0] for _ in range(7)]
self.supercategory = d[0]
if self.supercategory not in self.label:
self.categories.append(self.categorie())
self.label.append(self.supercategory)
# 边界框
x1 = int(d[-4]);
y1 = int(d[-3]);
x2 = int(d[-2]);
y2 = int(d[-1])
self.rectangle = [x1, y1, x2, y2]
self.bbox = [x1, y1, x2 - x1, y2 - y1] # COCO 对应格式[x,y,w,h]
self.annotations.append(self.annotation())
self.annID += 1
'''
sys.stdout.write('\n')
sys.stdout.flush()
def image(self):
image = {}
image['height'] = self.height
image['width'] = self.width
image['id'] = self.num + 1
image['file_name'] = self.filen_ame
return image
def categorie(self):
categorie = {}
categorie['supercategory'] = self.supercategory
categorie['id'] = len(self.label) + 1 # 0 默认为背景
categorie['name'] = self.supercategory
return categorie
def annotation(self):
annotation = {}
# annotation['segmentation'] = [self.getsegmentation()]
annotation['segmentation'] = [list(map(float, self.getsegmentation()))]
annotation['iscrowd'] = 0
annotation['image_id'] = self.num + 1
# annotation['bbox'] = list(map(float, self.bbox))
annotation['bbox'] = self.bbox
annotation['category_id'] = self.getcatid(self.supercategory)
annotation['id'] = self.annID
return annotation
def getcatid(self, label):
for categorie in self.categories:
if label == categorie['name']:
return categorie['id']
return -1
def getsegmentation(self):
try:
mask_1 = cv2.imread(self.path, 0)
mask = np.zeros_like(mask_1, np.uint8)
rectangle = self.rectangle
mask[rectangle[1]:rectangle[3], rectangle[0]:rectangle[2]] = mask_1[rectangle[1]:rectangle[3],
rectangle[0]:rectangle[2]]
# 计算矩形中点像素值
mean_x = (rectangle[0] + rectangle[2]) // 2
mean_y = (rectangle[1] + rectangle[3]) // 2
end = min((mask.shape[1], int(rectangle[2]) + 1))
start = max((0, int(rectangle[0]) - 1))
flag = True
for i in range(mean_x, end):
x_ = i;
y_ = mean_y
pixels = mask_1[y_, x_]
if pixels != 0 and pixels != 220: # 0 对应背景 220对应边界线
mask = (mask == pixels).astype(np.uint8)
flag = False
break
if flag:
for i in range(mean_x, start, -1):
x_ = i;
y_ = mean_y
pixels = mask_1[y_, x_]
if pixels != 0 and pixels != 220:
mask = (mask == pixels).astype(np.uint8)
break
self.mask = mask
return self.mask2polygons()
except:
return [0]
def mask2polygons(self):
contours = cv2.findContours(self.mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # 找到轮廓线
bbox=[]
for cont in contours[1]:
[bbox.append(i) for i in list(cont.flatten())]
# map(bbox.append,list(cont.flatten()))
return bbox # list(contours[1][0].flatten())
# '''
def getbbox(self, points):
# img = np.zeros([self.height,self.width],np.uint8)
# cv2.polylines(img, [np.asarray(points)], True, 1, lineType=cv2.LINE_AA) # 画边界线
# cv2.fillPoly(img, [np.asarray(points)], 1) # 画多边形 内部像素值为1
polygons = points
mask = self.polygons_to_mask([self.height, self.width], polygons)
return self.mask2box(mask)
def mask2box(self, mask):
'''从mask反算出其边框
mask：[h,w] 0、1组成的图片
1对应对象，只需计算1对应的行列号（左上角行列号，右下角行列号，就可以算出其边框）
'''
# np.where(mask==1)
index = np.argwhere(mask == 1)
rows = index[:, 0]
clos = index[:, 1]
# 解析左上角行列号
left_top_r = np.min(rows) # y
left_top_c = np.min(clos) # x
# 解析右下角行列号
right_bottom_r = np.max(rows)
right_bottom_c = np.max(clos)
# return [(left_top_r,left_top_c),(right_bottom_r,right_bottom_c)]
# return [(left_top_c, left_top_r), (right_bottom_c, right_bottom_r)]
# return [left_top_c, left_top_r, right_bottom_c, right_bottom_r] # [x1,y1,x2,y2]
return [left_top_c, left_top_r, right_bottom_c - left_top_c,
right_bottom_r - left_top_r] # [x1,y1,w,h] 对应COCO的bbox格式
def polygons_to_mask(self, img_shape, polygons):
mask = np.zeros(img_shape, dtype=np.uint8)
mask = PIL.Image.fromarray(mask)
xy = list(map(tuple, polygons))
PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
mask = np.array(mask, dtype=bool)
return mask
# '''
def data2coco(self):
data_coco = {}
data_coco['images'] = self.images
data_coco['categories'] = self.categories
data_coco['annotations'] = self.annotations
return data_coco
def save_json(self):
self.data_transfer()
self.data_coco = self.data2coco()
# 保存json文件
json.dump(self.data_coco, open(self.save_json_path, 'w'), indent=4) # indent=4 更加美观显示
xml_file = glob.glob('./Annotations/*.xml')
# xml_file=['./Annotations/000032.xml']
#xml_file=['00000007_05499_d_0000037.xml']
PascalVOC2coco(xml_file, 'train.json')
需要将所有的.xml放在Annotations文件下，根据自己的.xml格式进行修改