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labelme2COCO.py
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labelme2COCO.py
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# -*- 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
class labelme2coco(object):
def __init__(self, labelme_json=None, save_json_path='./new.json'):
"""
:param labelme_json: 所有labelme的json文件路径组成的列表
:param save_json_path: json保存位置
"""
self.labelme_json = labelme_json
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.data_coco = None
self.save_json()
def data_transfer(self):
"""
遍历所有labelme生成的json文件,进行COCO数据格式的转换
:return: 无
"""
for num, json_file in enumerate(self.labelme_json):
with open(json_file, 'r') as fp:
data = json.load(fp) # 加载json文件
self.images.append(self.image(data, num))
for shapes in data['shapes']:
label = shapes['label'].split('_')
if label[1] not in self.label:
self.categories.append(self.categorie(label))
self.label.append(label[1])
points = shapes['points']
self.annotations.append(self.annotation(points, label, num))
self.annID += 1
print(self.annID)
def image(self, data, num):
"""
获取图片基本信息
:param data: json格式标注文件转换后的Python标准字典
:param num: 索引
:return: image字典, keys: height, width, id, file_name
"""
image = {}
img = utils.img_b64_to_arr(data['imageData']) # 解析原图片数据
# img=io.imread(data['imagePath']) # 通过图片路径打开图片
# img = cv2.imread(data['imagePath'], 0)
height, width = img.shape[:2]
img = None
image['height'] = height
image['width'] = width
image['id'] = num + 1
image['file_name'] = data['imagePath'].split('/')[-1]
self.height = height
self.width = width
return image
def categorie(self, label):
"""
获取图片种类信息
:param label: 按照"_"分割之后的字符串列表
:return: categorie字典, keys: id, name, supercategory
"""
categorie = dict()
categorie['supercategory'] = label[0]
categorie['id'] = len(self.label) + 1 # 0 默认为背景
categorie['name'] = label[1]
return categorie
def annotation(self, points, label, num):
"""
获取图片标注信息
:param points: 标注区域点的集合
:param label: 按照"_"分割之后的字符串列表
:param num: 索引
:return: annotation字典, keys: id, iamge_id, category_id, segmentation, bbox, iscrowd
"""
annotation = dict()
annotation['segmentation'] = [eval(str(list(np.asarray(points, dtype=np.float32).flatten())))]
annotation['iscrowd'] = 0
annotation['image_id'] = num + 1
# annotation['bbox'] = str(self.getbbox(points)) # 使用list保存json文件时报错（不知道为什么）
# list(map(int,a[1:-1].split(','))) a=annotation['bbox'] 使用该方式转成list
annotation['bbox'] = list(map(float, self.getbbox(points)))
annotation['category_id'] = self.getcatid(label)
annotation['id'] = self.annID
annotation['area'] = self.poly_area(points)
return annotation
def getcatid(self, label):
"""
获取标签对应的categoriy_id
:param label: 按照"_"分割之后的字符串列表
:return:
"""
for categorie in self.categories:
if label[1] == categorie['name']:
return categorie['id']
return -1
def getbbox(self, points):
"""
获取标注区域点所对应的bounding_box
:param points: 标注区域点的集合
:return: [x1, y1, w, h]
"""
# 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):
"""
将组成多边形点的列表转换为掩膜
:param img_shape: 图片的形状
:param polygons: 组成多边形点的列表
:return: 掩膜
"""
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):
"""
将数据封装成为coco格式
:return: data_coco, coco格式的数据
"""
data_coco = dict()
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
def poly_area(self, points):
"""
计算多边形面积
:param points: 标注文件中points列表
:return: 对应多边形面积
"""
x_list = [coord[0] for coord in points]
y_list = [coord[1] for coord in points]
x = np.array(x_list)
y = np.array(y_list)
return 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
labelme_json = glob.glob('./*.json')
# labelme_json=['./1.json']
labelme2coco(labelme_json, './new.json')