Lableme2mpii
import numpy as np
import json
import glob
import random
class MyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(MyEncoder, self).default(obj)
class tompii(object):
def __init__(self, jsonfile, save_path):
self.annotations = []
self.jsonfile=jsonfile
self.save_path=save_path
self.cnt=0
def check(self,data):
if len(data['shapes']) % 17 !=0:
return False
for i in range(len(data['shapes'])):
if i % 17 ==0 and data['shapes'][i]['label']!='obj':
return False
if i % 17 != 0 and data['shapes'][i]['label'] not in ['0','1','2']:
return False
return True
def get_cattle_width(self,obj):
return obj['points'][1][0] - obj['points'][0][0]
def get_cattle_height(self,obj):
return obj['points'][1][1] - obj['points'][0][1]
def get_objpos(self,obj):
return [round((obj['points'][0][0]+obj['points'][1][0]/2),3),round((obj['points'][0][1]+obj['points'][1][1]/2),3)]
def labelme_to_mpii(self):
annid = 0
for num, json_file in enumerate(self.jsonfile):
annotation={}
data = open(json_file, "r")
data = json.load(data)
if self.check(data) != True:
print(json_file)
continue
self.cnt+=1
annotation['img_paths']=json_file.split('\\')[1].split('.')[0]+".jpg"
cattleNum = len(data['shapes']) // 17
for main in range(cattleNum):
cattle_width=round(self.get_cattle_width(data['shapes'][17*main]),3)
cattle_height=round(self.get_cattle_height(data['shapes'][17*main]),3)
scale_provided=round(data['imageWidth']/cattle_width,3)
annotation['objpos']=self.get_objpos(data['shapes'][17*main])
annotation['validation']=0.0
shapes = data["shapes"]
joint_self=[]
for i in range(17*main+1,17*main+17):
point=[round(shapes[i]['points'][0][0],3),round(shapes[i]['points'][0][1],3),eval(shapes[i]['label'])]
joint_self.append(point)
annotation['joint_self']=joint_self
annotation['scale_provided']=scale_provided
if cattleNum==1:
numOtherCattle=0.0
objpos_other=None
joint_others=None
scale_provided_other=None
annotation['numOtherCattle']=numOtherCattle
annotation['objpos_other']=objpos_other
annotation['joint_others']=joint_others
annotation['scale_provided_other']=scale_provided_other
else:
nuuOtherCattle=cattleNum-1
objpos_other=[]
joint_others=[]
scale_provided_other=[]
for i in range(cattleNum):
if i == main:
continue
cattle_width = round(self.get_cattle_width(data['shapes'][i*17]), 3)
cattle_height = round(self.get_cattle_height(data['shapes'][i*17]), 3)
scale_provided_other.append(round(data['imageWidth'] / cattle_width,3))
objpos_other.append(self.get_objpos(data['shapes'][i*17]))
joint_other=[]
for j in range(1,17):
point = [round(shapes[i*17+j]['points'][0][0], 3), round(shapes[i*17+j]['points'][0][1], 3),
eval(shapes[i*17+j]['label'])]
joint_other.append(point)
joint_others.append(joint_other)
annotation['numOtherCattle'] = numOtherCattle
annotation['objpos_other'] = objpos_other
annotation['joint_others'] = joint_others
annotation['scale_provided_other'] = scale_provided_other
self.annotations.append(annotation)
random.shuffle(self.annotations)
length=len(self.annotations)
for i in self.annotations[-int(length*0.2):-1]:
i['validation']=1.0
print(self.annotations[-int(length*0.2):-1])
def save_json(self):
self.labelme_to_mpii()
mpii_annotations = self.annotations
# 保存json文件
json.dump(mpii_annotations, open(self.save_path, 'w'), indent=4, cls=MyEncoder) # indent=4 更加美观显示
import glob
labelme_json = glob.glob('json/*.json')
c = tompii(labelme_json, 'new_annotations.json')
c.save_json()
